Accurate, dense, and robustmulti-view stereopsis
- 格式:pdf
- 大小:781.13 KB
- 文档页数:8
介绍北斗卫星导航系统的研发应用英语作文全文共3篇示例,供读者参考篇1Introduction to Beidou Satellite Navigation SystemThe Beidou Satellite Navigation System, also known as the BeiDou-3 system, is a global navigation satellite system developed by China. It consists of a network of satellites that provide positioning, navigation, and timing services to users worldwide. The system is designed to provide accurate, reliable, and continuous navigation services to users in various fields, including aviation, maritime, agriculture, transportation, and disaster relief.The Beidou Satellite Navigation System is a significant achievement in China's space technology and has been in development for more than two decades. The system aims to reduce the country's dependence on foreign satellite navigation systems, such as GPS, and to establish China as a major player in the global satellite navigation market.The Beidou Satellite Navigation System is based on a constellation of satellites in medium Earth orbit (MEO) andgeostationary orbit (GEO). The system currently consists of 35 operational satellites, with plans to expand to 35 satellites by 2020. The satellites are equipped with advanced navigation and positioning technology, including atomic clocks, onboard processors, and communication systems.The Beidou Satellite Navigation System offers a range of services, including precise positioning, navigation, and timing services. The system provides positioning accuracy of less than 10 meters, navigation accuracy of less than 0.2 meters per second, and timing accuracy of less than 50 nanoseconds. These services are crucial for various applications, including vehicle navigation, transportation logistics, precision agriculture, and disaster response.One of the key advantages of the Beidou Satellite Navigation System is its compatibility with other global navigation satellite systems, such as GPS, GLONASS, and Galileo. This interoperability allows users to receive signals from multiple satellite systems simultaneously, enhancing the accuracy and reliability of positioning and navigation services. The system also supports dual-frequency signals, which further improve accuracy in challenging environments, such as urban canyons and forested areas.The Beidou Satellite Navigation System is being widely used in China and is gradually expanding its services to countries along the Belt and Road Initiative. The system has been adopted in various sectors, including transportation, agriculture, surveying, mapping, disaster relief, and scientific research. The system has also been integrated into smartphones, tablets, and other consumer devices, making satellite navigation services more accessible to the general public.In conclusion, the Beidou Satellite Navigation System is a significant technological achievement that demonstrates China's capabilities in space technology. The system offers accurate, reliable, and continuous navigation services to users worldwide, supporting a wide range of applications in various sectors. With its expanding constellation of satellites and advanced technology, the Beidou Satellite Navigation System is poised to become a key player in the global satellite navigation market.篇2IntroductionThe Beidou Satellite Navigation System, also known as Compass Navigation System, is a Chinese satellite navigation system that provides accurate positioning, navigation, andtiming services to users worldwide. It was developed by China Satellite Navigation Office and is named after the Chinese term for the Big Dipper constellation.History of Beidou Satellite Navigation SystemThe development of the Beidou Satellite Navigation System began in the 1990s as China's answer to the US GPS system. The first experimental Beidou satellite was launched in 2000, and the system gradually became fully operational with the launch of additional satellites over the years. By 2020, China has deployed a constellation of 35 Beidou satellites in orbit, providing global coverage for its users.Features of Beidou Satellite Navigation SystemThe Beidou Satellite Navigation System offers several key features that make it a valuable asset for various applications. These features include:- High accuracy: The Beidou system provides positioning accuracy of up to 10 meters, making it suitable for a wide range of applications that require precise location information.- Global coverage: With its constellation of satellites, the Beidou system offers global coverage, ensuring that users can access its services anywhere in the world.- Multiple services: In addition to providing positioning and navigation services, the Beidou system also offers timing synchronization services that are crucial for a wide range of industries.- Dual-frequency signals: The Beidou system utilizesdual-frequency signals, which can improve accuracy and reliability, especially in challenging environments such as urban canyons or dense forests.Applications of Beidou Satellite Navigation SystemThe Beidou Satellite Navigation System has a wide range of applications across various industries, including:- Transportation: The Beidou system is widely used in the transportation sector for vehicle tracking, route planning, and navigation. It helps improve the efficiency of logistics operations and enhances the safety of vehicles on the road.- Precision agriculture: By using the Beidou system, farmers can accurately monitor their crops, optimize irrigation and fertilizer application, and improve overall crop yield.- Disaster management: The Beidou system plays a crucial role in disaster management by providing accurate positioninginformation that helps rescue teams locate and assist people in distress during emergencies.- Maritime navigation: The Beidou system is essential for maritime navigation, providing ships with accurate positioning information to ensure safe and efficient navigation on the seas.Future DevelopmentsChina is continuously investing in the development of the Beidou Satellite Navigation System to enhance its capabilities and expand its applications. Future developments of the system may include:- Enhanced positioning accuracy: China aims to improve the positioning accuracy of the Beidou system to meet the growing demands of various industries that require high-precision location information.- Integration with other systems: China is exploring opportunities to integrate the Beidou system with other satellite navigation systems, such as GPS and Galileo, to enhance interoperability and provide users with more reliable and robust positioning services.- Expansion of services: China plans to expand the services offered by the Beidou system to meet the diverse needs of users,including the development of new applications and services in areas such as smart cities, autonomous vehicles, and Internet of Things (IoT) devices.ConclusionThe Beidou Satellite Navigation System is a valuable asset for China and the global community, providing accurate positioning, navigation, and timing services that support a wide range of applications across various industries. With its high accuracy, global coverage, and multiple services, the Beidou system has become an essential tool for improving efficiency, safety, and productivity in different sectors. As China continues to invest in the development of the Beidou system, we can expect to see even more advancements and innovations that will further enhance its capabilities and benefits for users worldwide.篇3Development and Application of the Beidou Satellite Navigation SystemIntroductionThe Beidou Satellite Navigation System, also known as the BeiDou-3 system, is a Chinese satellite navigation system that aims to provide global coverage by 2020. It is a significantmilestone in China's space technology development and has a wide range of applications in various sectors such as transportation, agriculture, telecommunications, and disaster management.Development of the Beidou SystemThe development of the Beidou Satellite Navigation System began in the early 1990s with the launch of the first generation of Beidou satellites. Over the years, China has made significant advancements in the system's technology and infrastructure to improve its accuracy, reliability, and coverage.The Beidou system consists of three major components: the space segment, the ground segment, and the user equipment. The space segment includes a constellation of satellites in geostationary orbit, medium Earth orbit, and inclined geosynchronous orbit to provide global coverage. The ground segment consists of monitoring stations, control centers, and data processing facilities to track and manage the satellites. The user equipment, such as smartphones, car navigation systems, and handheld devices, receives signals from the satellites to determine the user's location accurately.Applications of the Beidou SystemThe Beidou Satellite Navigation System has a wide range of applications across various industries:1. Transportation: The Beidou system is widely used in the transportation sector for vehicle tracking, route planning, and traffic management. It helps improve the accuracy and efficiency of logistics operations, reduces travel time, and enhances overall safety on roads, railways, and waterways.2. Agriculture: The Beidou system plays a crucial role in precision agriculture by providing farmers with accurate location data for crop monitoring, irrigation, and fertilization. It improves crop yields, reduces resource wastage, and supports sustainable farming practices.3. Telecommunications: The Beidou system enables precise timing and synchronization for telecommunications networks, mobile communication systems, and internet services. It enhances network performance, reduces interference, and ensures seamless connectivity for users.4. Disaster Management: The Beidou system is utilized for disaster monitoring, early warning, and emergency response during natural disasters such as earthquakes, tsunamis, and floods. It helps coordinate rescue efforts, locate survivors, and deliver aid to affected areas promptly.5. Surveying and Mapping: The Beidou system provides high-accuracy positioning, navigation, and mapping services for surveying, mapping, and geospatial applications. It supports land surveying, urban planning, infrastructure development, and environmental conservation initiatives.ConclusionThe Beidou Satellite Navigation System has emerged as a competitive player in the global satellite navigation market, offering advanced technology, extensive coverage, and diverse applications. Its development and application in various sectors demonstrate China's commitment to innovation, technological advancement, and international cooperation in space exploration. As the Beidou system continues to evolve and expand, it will undoubtedly contribute to the advancement of society, economy, and sustainable development on a global scale.。
建康的生活对年轻人很重要英语作文全文共3篇示例,供读者参考篇1Sure, here's an essay on "A Healthy Lifestyle is Important for Young People" written in the voice of a student, approximately 2000 words long:A Healthy Lifestyle is Important for Young PeopleAs a student, I can't stress enough how important it is to maintain a healthy lifestyle. It's crucial for our overall well-being, and it sets the foundation for a successful future. In this essay, I'll explore the various aspects of a healthy lifestyle and why it's so vital for young people like me.First and foremost, let's talk about physical health. We've all heard the saying, "Health is wealth," and it couldn't be more accurate. A healthy body is essential for tackling the demands of our daily lives, whether it's attending classes, participating in extracurricular activities, or simply enjoying our youth. Regular exercise is key to maintaining physical fitness, and it doesn't have to be a chore. Finding activities that we genuinely enjoy, likeplaying sports, dancing, or even going for a brisk walk, can make exercise feel like a fun pastime rather than a tedious task.But physical health isn't just about exercise; it's also about what we put into our bodies. A balanced and nutritious diet is crucial for young people like us. We're in a phase of rapid growth and development, and our bodies require the right nutrients to function optimally. Cutting back on processed foods, sugary drinks, and unhealthy snacks, and instead opting for whole foods, fruits, vegetables, and lean proteins, can make a significant difference in our energy levels, concentration, and overallwell-being.Mental health is another crucial aspect that often gets overlooked, especially among young people. The pressures of school, social life, and the uncertainties of the future can take a toll on our emotional well-being. Developing healthy coping mechanisms, such as practicing mindfulness, seeking support from loved ones, or even pursuing counseling when needed, can help us navigate these challenges more effectively.Furthermore, a healthy lifestyle isn't just about physical and mental well-being; it's also about cultivating positive habits and discipline. By making conscious choices to prioritize our health, we're developing valuable life skills that will serve us well in thelong run. Time management, self-discipline, and the ability to set and achieve goals are all qualities that can be honed through the pursuit of a healthy lifestyle.Beyond the personal benefits, adopting a healthy lifestyle can also have a positive impact on our academic performance. Studies have shown that students who exercise regularly and maintain a balanced diet tend to perform better academically. Physical activity has been linked to improved focus, concentration, and cognitive function, while a nutritious diet provides the necessary fuel for our brains to function at their best.Moreover, a healthy lifestyle can contribute to our social well-being. Engaging in physical activities or joining sports teams can foster a sense of community and camaraderie, allowing us to build meaningful connections with our peers. Additionally, maintaining good physical and mental health can boost our self-confidence and self-esteem, which can positively impact our social interactions and relationships.It's important to acknowledge that embracing a healthy lifestyle can be challenging, especially in the face of temptations and societal pressures. However, it's crucial to remember that small, consistent steps can lead to significant long-term benefits.Whether it's taking the stairs instead of the elevator, packing a healthy lunch instead of grabbing fast food, or setting aside time for a relaxing activity like reading or meditating, every positive choice counts.In conclusion, a healthy lifestyle is paramount for young people like me. It encompasses physical, mental, and social well-being, and it lays the foundation for a successful and fulfilling future. By prioritizing our health through regular exercise, a balanced diet, mindfulness practices, and cultivating positive habits, we're investing in ourselves and setting ourselves up for a lifetime of prosperity. It's a journey, and it may not always be easy, but the rewards of a healthy lifestyle are invaluable. Let's embrace this opportunity to thrive, not just survive, and make our health a top priority.篇2A Healthy Lifestyle is Important for Young PeopleAs a student, I can't emphasize enough how crucial it is to maintain a healthy lifestyle during our youth. We often take our health for granted, thinking we're invincible and that our bodies can withstand anything. However, the habits we develop in ouryounger years can have a profound impact on our overallwell-being, both in the present and in the long run.In today's fast-paced world, it's easy to get caught up in the hustle and bustle of academic responsibilities, social engagements, and digital distractions. We often neglect our physical and mental health, opting for convenient but unhealthy choices, such as skipping meals, staying up late binge-watching shows, or indulging in junk food. While these habits may seem harmless in the moment, they can accumulate over time and lead to serious health issues.One of the most significant aspects of a healthy lifestyle is maintaining a balanced diet. As young people, we need a wide range of nutrients to support our growth, development, and overall energy levels. A diet rich in fruits, vegetables, whole grains, and lean proteins can provide us with the necessary vitamins, minerals, and fiber to keep our bodies functioning optimally. Additionally, staying hydrated by drinking plenty of water is crucial for maintaining good health.Regular physical activity is another essential component of a healthy lifestyle. Exercise not only helps us maintain a healthy weight, but it also strengthens our cardiovascular system, builds muscular strength, and improves our overall fitness levels.Engaging in activities we enjoy, such as sports, dancing, or even brisk walking, can make exercise feel less like a chore and more like a fun and rewarding experience.Furthermore, getting enough sleep is vital for our physical and mental well-being. During sleep, our bodies repair and rejuvenate themselves, and our minds process and consolidate information from the day. Insufficient sleep can lead to fatigue, irritability, and impaired cognitive function, which can negatively impact our academic performance and overall quality of life.Mental health is often overlooked when discussing a healthy lifestyle, but it is equally crucial, especially for young people. The pressures of academic demands, social expectations, and future uncertainties can take a toll on our mental well-being. It's essential to prioritize self-care activities, such as meditation, journaling, or seeking support from friends, family, or professionals when needed. Building resilience and developing coping mechanisms can help us navigate the challenges of youth and maintain a positive mindset.Beyond the physical and mental aspects, a healthy lifestyle also encompasses fostering positive relationships and engaging in meaningful activities. Surrounding ourselves with supportive and uplifting individuals can provide a sense of belonging andfulfillment. Additionally, pursuing hobbies, volunteering, or participating in extracurricular activities can enrich our lives, broaden our perspectives, and contribute to our overallwell-being.It's important to recognize that adopting a healthy lifestyle is a journey, not a destination. It's a continuous process of making conscious choices and developing sustainable habits. There will be moments of temptation and setbacks, but what's essential is to approach these challenges with kindness and perseverance. Celebrating small victories and recognizing our progress can motivate us to stay on track and continue striving for a healthier life.In conclusion, a healthy lifestyle is paramount for young people like ourselves. By prioritizing a balanced diet, regular exercise, adequate sleep, mental well-being, and fostering positive relationships and activities, we lay the foundation for a life filled with energy, vitality, and overall well-being. It's a lifelong investment that not only benefits us in the present but also sets us up for a healthier and more fulfilling future. Let's embrace the power of a healthy lifestyle and make conscious choices that nurture our bodies, minds, and souls.篇3A Healthy Lifestyle: The Key to a Fulfilling YouthAs a student, I can't emphasize enough how crucial it is for young people like me to adopt and maintain a healthy lifestyle. In the midst of academic pressures, social commitments, and the temptations of modern life, it's easy to neglect our well-being. However, the choices we make today will have a profound impact on our physical, mental, and emotional health, shaping our overall quality of life in the years to come.Physical Health: The Foundation of VitalityLet's start with the most tangible aspect: physical health. A balanced diet and regular exercise are the cornerstones of a robust physique. In our fast-paced world, it's tempting to rely on convenient but unhealthy options like fast food and sugary drinks. However, these choices can lead to obesity, diabetes, and other health issues that can significantly impair our quality of life. Instead, we should prioritize nutrient-dense foods, such as fruits, vegetables, whole grains, and lean proteins, which provide the energy and nourishment our bodies need to thrive.Exercise is equally important. Regular physical activity not only helps us maintain a healthy weight but also strengthens our cardiovascular system, bones, and muscles. It's a misconception that exercise is solely about losing weight or building muscle; it'sa holistic approach to enhancing our overall well-being. Whether it's joining a sports team, going for a jog, or taking a dance class, finding an activity we enjoy can make exercise a fun and rewarding experience.Mental Health: Nurturing the MindIn addition to physical health, nurturing our mentalwell-being is crucial for a fulfilling youth. The pressures of academics, social media, and societal expectations can take a toll on our mental state. Stress, anxiety, and depression are becoming increasingly prevalent among young people, and if left unchecked, can have severe consequences on our overall development and quality of life.One effective way to alleviate mental strain is through mindfulness practices such as meditation, yoga, or simply taking a few moments each day to breathe deeply and clear our minds. These practices have been proven to reduce stress levels, improve focus, and cultivate a sense of inner peace and resilience.Additionally, seeking support from professionals, trusted friends, or family members can be invaluable in navigating the complexities of mental health challenges. It's essential torecognize that seeking help is a sign of strength, not weakness, and that we all need a supportive network to thrive.Emotional Well-being: Cultivating ResilienceClosely tied to mental health is emotional well-being, which plays a vital role in shaping our overall happiness and resilience. As young people, we often face numerous challenges, setbacks, and disappointments that can test our emotional fortitude. Learning to manage our emotions healthily is a critical skill that will serve us well throughout our lives.Developing self-awareness, practicing mindfulness, and cultivating self-compassion can help us navigate the ups and downs of life with greater grace and resilience. Instead of getting caught up in negative thought patterns or self-criticism, we can learn to be kind and understanding towards ourselves, recognizing that imperfection and growth are natural parts of the human experience.Building strong, supportive relationships is also crucial for emotional well-being. Surrounding ourselves with positive, uplifting individuals who genuinely care about our happiness can provide a sense of belonging and purpose, which are essential for emotional fulfillment.The Ripple Effect: Impacting Society and the EnvironmentWhile the benefits of a healthy lifestyle may seem personal, the impacts extend far beyond the individual. By prioritizing our well-being, we contribute to the collective health and prosperity of our communities and society as a whole.For instance, adopting sustainable and eco-friendly practices, such as reducing waste, conserving energy, and supporting local agriculture, can have a profound impact on the environment. By making conscious choices, we can reduce our carbon footprint and contribute to a healthier planet for future generations.Furthermore, prioritizing our well-being can inspire others around us to do the same, creating a ripple effect of positive change. When we lead by example and demonstrate the benefits of a healthy lifestyle, we can influence our peers, families, and communities to embrace similar values and practices.The Path Forward: Embracing a Holistic ApproachAchieving a truly healthy lifestyle is a lifelong journey that requires patience, dedication, and a holistic approach. It's not about adhering to strict rules or depriving ourselves of enjoyment; rather, it's about finding a balanced and sustainableway of living that nourishes our physical, mental, and emotional needs.While the path may not always be easy, the rewards of a healthy lifestyle are immeasurable. By prioritizing our well-being, we can unlock our full potential, cultivate resilience, and experience a deep sense of fulfillment and joy in our youth and beyond.So, let us embrace this journey together, supporting each other along the way. Let us make conscious choices that nurture our bodies, minds, and spirits. For in doing so, we not only enhance our own lives but also contribute to the betterment of our communities and the world around us.。
一种基于置信度的深度图融合方法董鹏飞【摘要】在三维重建过程中,由于受到噪声影响,计算出的深度图精度无法保证.针对此问题,提出一种基于置信度的抗噪融合方法.首先对每幅深度图来进行修正,并利用一致性原理来剔除错误点并填补某些空洞.然后通过保留在自身邻域内具有最高置信度的三维点,以删除冗余.最后将深度图反投影到三维空间,采用迭代最小二乘法优化三维点并剔除离群点.通过在测试数据集上与其他算法比较,验证此方法的有效性.【期刊名称】《现代计算机(专业版)》【年(卷),期】2016(000)035【总页数】4页(P66-69)【关键词】多目立体视觉;三维重建;深度图融合【作者】董鹏飞【作者单位】四川大学计算机学院,成都610065【正文语种】中文多视角三维重建的目的是从多幅二维图像当中恢复目标场景的3D模型,是计算机视觉领域中重要的研究课题之一,并且在最近几年也受到了越来越多的关注。
根据Seitz等人的研究[1],现有的多视角三维重建算法可以分为四类:基于特征点扩展的方法[2],该方法首先提取和匹配一系列特征点重建出较稀疏的点云,然后将稀疏点云向周围扩展得到稠密的点云;基于体素的方法[3],该方法在一个三维立体中计算出代价函数,并且从这些立体中提取出目标物体表面;基于表面演化的方法[4],该方法通过最小化能量方程来迭代估计出目标物体表面;基于深度图融合的方法[5-6],该方法首先计算出每幅图像的深度图,然后通过融合这些深度图来获取最终的三维模型。
在所有的算法中,基于深度图融合算法具有较高的精确度和灵活性,更适用于绝大多数场景的重建[1,5]。
一般来说,基于深度图融合的三维重建方法可以分为两个步骤:①计算每幅图像所对应的深度图。
②将这些深度图融合成为一个三维模型。
目前,已有许多研究者在深度图计算方面做出了杰出的工作。
Goesele等人利用了基于窗口的投票方法,但是这种方法只能得到匹配度较高的像素对[7]。
Bradley等人通过尺度可变的窗口来增加匹配的像素对,从而使深度计算更加准确[8]。
HOW TOSYNCHRONIZEa Honeywell HGuide n580 navigator with a Velodyne HDL-32E or VLP-16 LiDAR unitABOUTHONEYWELL AEROSPACEHoneywell Aerospace innovates and integrates thousands of products, software, and services to advance and more easilydeliver safe, efficient, productive, and comfortable transportation experiences worldwide. Our offerings are found on virtually every commercial, defense, and space aircraft.We develop innovative solutions for more fuel-efficient and environmentally-friendly airplanes, more direct and on-time flights, safer flying and reduced runway and flight traffic plus engines, cockpit and cabin electronics, wireless connectivity equipment and services, and logistics.In 2016, Honeywell launched a new business unit to bring a new range of class-leading IMUs and navigators to market. These are available with no export license (ECCN = 7A994), bringing ourtraditional aerospace quality and design standards to a new range of customers at industrial pricing.To learn more about the Honeywell’s HGuide IMUs and navigators, please visit: /hguide.Honeywell Aerospace 2600 Ridgway Parkway Minneapolis MN 55413N61-2330-000-000 | 10/19Author of this document:Darren Fisher Sales ManagerAPAC and United KingdomTalk to an ExpertFor North America and South America:Ray SturmRay.S.Sturm@ +1 6122804010For Asia Pacific and the United Kingdom:Darren FisherDarren.Fisher@ +44 (0)7779970095For Mainland Europe,the Middle East, Africa and India:Theo Kuijper van der DuijnTheo.KuijpervanderDuijn@ +41 (0)1217117904TABLE OF CONTENTS 3 Executive Summary 4 Synchronization Concept 6 Process to Set Up Sync 7 Conclusion8 About Honeywell Aerospace 8 Talk to an ExpertOur diverse suite of non-itar, commercial HGuide inertial measurement units (IMUs) and navigators provide the same technology and are available today for several industrial applications including but not limited to agriculture, AUVs, communications, industrial equipment, marine, oil and gas, robotics, survey and mapping, stabilized platforms, transportation, UAVs and UGVs.The Honeywell HGuide n580 is a small, light-weight, self-contained, all-attitude inertial navigation system (INS)/global navigation satellite system (GNSS) navigator that provides position and orientation information, even when GPS/GNSS signals aren’t available.The HGuide n580 INS/GNSS contains Honeywell’s leading edge HG4930 IMU and provides a powerful dual-antenna, multi-frequency, multi-constellation real-timekinematic (RTK) capability. Honeywell’s integration expertise blends the IMU and GNSS data to provide accurate, robust navigation data.Mobile ApplicationsUsers needing to collect and process light detection and ranging (LiDAR) data from a static location need to know the location of the scanner to anchor the data logged and subsequent 3D image to a real latitude, longitude and height. It is usually assumed that the scanner has been set up so it is level.If this scanning is performed from a moving platform like a car or aerial vehicle the location and attitude of the vehicle will constantly change over time. Thus, the user will need to understand a few key parameters about the host vehicle:– Latitude, longitude and altitude at the precise time the LiDAR data is valid – Attitude at the precise time the LiDAR data is valid – Time the LiDAR data is validThese critical parameters are needed to use the point cloud data to build an accurate 3D map of the recorded environment.Honeywell has been producing high-performance inertial sensors for decades and has delivered more than 500,000 units to serve as navigation aids on just about every airplane and spacecraft flying today.EXECUTIVE SUMMARYAn INS/GNSS is the key to understanding the vehicle dynamics during the data collection.An INS/GNSS typically comprises a GNSS receiver, an IMU and data fusion software (e.g., Kalman Filter) to blend the GNSS and IMU data. Often this is supplemented with an odometer which provides vehicle speed to the data fusion software.This navigation solution may be used in real-time. In other cases, raw data files from the INS/GNSS are logged for later processing. This paper will discuss the data-logging variant, however the LiDAR synchronization methodology is identical.Using an INS/GNSS, the user will understand precise position, time and attitude at each point of the data collection run. Adding an odometer as a 3rd sensor improves this solution when the GNSS signal is not available, for example in tunnels or dense urban canyons.The Honeywell HGuide n580 is ideal for applications with these requirements.a Honeywell HGuide n580 navigator with a Velodyne HDL-32E or VLP-16 LiDAR unitHOW TOSYNCHRONIZESYNCHRONIZATION CONCEPTTo merge the INS/GNSS and LiDAR data correctly, it is essential that the timestamps on the INS/GNSS and LiDAR data files aresynchronized to a common reference clock. Typically, the synchronization between the data logged by the INS/GNSS and the LiDAR unit is achieved by using GPS time as the primary clock.To effectively timestamp the Velodyne HDL-32E or VLP-16 LiDAR data file only requires two pieces of information from the INS/GNSS:– A TTL level ‘1 pulse per second’ (1PPS) signal. This is required to be sent precisely every second.– NMEA $GPRMC message, which contains the actual time of the 1PPS.The HGuide n580 output messages and relative timing of these signals has been designed and tested to be easily integrated with the Velodyne HDL-32E and VLP-16 as well as many other LiDAR scanners.This diagram shows how the major building blocks of the system are connected. The GNSS antennae provide an RF signal into the HGuide n580. The HGuide n580 provides both a 1PPS and real time GNSS data, including GPS time to the Velodyne unit.Figure 1: HGuide n580 -> LiDAR Interface Box -> LiDAR Diagram– Once these connections have been made, power up the HGuide n580 and Velodyne LiDAR unit, ensuring the GNSS antennae connected to the HGuide n580 have a good view of the sky. This is needed to ensure it receives a satellite signal.– Ensure the GNSS board in the HGuide n580 has acquired ‘GNSS Lock,’ by using the HGuide Data Reader program to verify status. The screen shot below shows latitude, longitude and altitude in the ‘position’ window. This indicates that the GNSS receiver has a GNSS position lock.SIGNAL$GPRMC Message IO Port102102102Pin Number Pin 15Pin 14Pin 9Pin Label Ground GPS Receive GPS PulseHGUIDE N580VELODYNE INTERFACE BOXFigure 2. HGuide n580 to Velodyne Interface Box wiring tableFigure 3: HGuide n580 Provides Accurate Navigation Data Driving Under an OverpassFigure 4: Velodyne LiDAR Software Showing GPS Position and PPS ‘locked’Synchronizing the Honeywell HGuide n580 and Velodyne VLP-16 or VLP-32E is a quick, simple task, enabling users to sync the files recorded by the LiDAR to those being logged from the HGuide n580.The HGuide n580 output data includes time stamped position, velocity, angular rate, linear acceleration, roll, pitch and heading information. In dual-antenna mode, the device supports GNSS-based heading measurements and initialization and has been specifically designed with a broad range of inputs and outputs to quickly and easily interface with many other sensors.Among these are the Velodyne VLP-16 and VLP-32E LiDAR units. The outputs required for this integration are available by default and users can quickly and easily connect the two hardware units and be ready to collect data.。
Oracle Server X9-2LOracle Server X9-2L is the ideal 2U platform for databases, enterprise storage, and big data solutions. Supporting the standard and enterprise editions of Oracle Database, this server delivers best-in-class database reliability in single-node configurations. With support for up to 132.8 TB of high-bandwidth NVM Express (NVMe) flash storage, Oracle Database using its Database Smart Flash Cache feature, as well as NoSQL and Hadoop applications can be significantly accelerated. Optimized for compute, memory, I/O, and storage density simultaneously, Oracle Server X9-2L delivers extreme storage capacity at lower cost when combined with Oracle Linux, or Oracle Solaris with ZFS file system compression. Each server comes with built-in, proactive fault detection and advanced diagnostics, along with firmware that is already optimized for Oracle software, to deliver extreme reliability.Product OverviewOracle Server X9-2L is a two-socket server designed and built specifically for the demands of enterprise workloads. It is a crucial building block in Oracle engineered systems and Oracle Cloud Infrastructure. Powered by one Platinum, two Gold, or one Silver Intel® Xeon® Scalable Processor Third Generation models with up to 32 cores per socket, along with 32 memory slots, this server offers high-performance processors plus the most dense flash storage options in a 2U enclosure. Oracle Server X9-2L is the most balanced and highest performing 2U enterprise server in its class because it offers optimal core and memory density combined with high I/O throughput.In addition to optimized processing power and storage density, Oracle ServerX9-2L offers 10 PCIe 4.0 expansion slots (two 16-lane and eight 8-lane) for maximal I/O card and port density. With 576 gigabytes per second of bidirectional I/O bandwidth, Oracle Server X9-2L can handle the most demanding enterprise workloads.Oracle Server X9-2L offers best-in-class reliability, serviceability, and availability (RAS) features that increase overall uptime of the server. This extreme reliability makes Oracle Server X9-2L the best choice for single-node Oracle Database deployments in remote or branch office locations. Real-time monitoring of the health of the CPU, memory, and I/O subsystems, coupled with off lining capability of failed components, increases the system availability. Building on the firmware-level problem detection, Oracle Linux and Oracle Solaris are enhanced to provide fault detection capabilities when running on Oracle Server X9-2L. In Key FeaturesMost flash-dense andenergy-efficient 2Uenterprise-class serverTwo Intel® Xeon® Scalable Processor Third GenerationCPUsThirty-two DIMM slots with maximum memory of 2 TB Ten PCIe Gen 4 slotsUp to 216 TB SAS-3 disk storage in 12 slots instandard configurationsUp to 132.8 TB NVM Express high-bandwidth all-flashconfigurationOracle Integrated Lights Out Manager (ILOM)Key BenefitsReduce vulnerability tocyberattacksAccelerate Oracle Database, NoSQL, and Hadoopapplications using Oracle’sunique NVM Express design Satisfy demands ofenterprise applications withextreme I/O card densityIncrease uptime with built-in diagnostics and faultdetection from Oracle Linuxand Oracle SolarisIncrease storage capacity 15x compared to previousgeneration, combiningextreme compute power with Oracle Solaris and ZFScompressionMaximize system power efficiency with OracleAdvanced System CoolingMaximize IT productivity by running Oracle software onOracle hardwareaddition, exhaustive system diagnostics and hardware-assisted error reporting and logging enable identification of failed components for ease of service.To help users achieve accelerated performance of Oracle Database, Oracle Server X9-2L supports hot-swappable, high-bandwidth flash that combines with Database Smart Flash Cache to drive down cost per database transaction. In the all-flash configuration, with Oracle’s unique NVM Express design, Oracle Server X9-2L supports up to 12 small form factor NVMe drives and up to eight NVMe add-in cards, for a total capacity of 132.8 TB. This massive flash capacity also benefits NoSQL and Hadoop applications, reducing network infrastructure needs and accelerating performance with 120 GB per second of total NVMe bidirectional bandwidth.For maximizing storage capacity, Oracle Server X9-2L is also offered in a standard 12-disk configuration, with 3.5-inch large form factor disk slots accommodating high-capacity hard disk drives (HDDs). A maximum 216 TB of direct-attached storage makes Oracle Server X9-2L ideally suited as a storage server. The compute power of this server can be used to extend storage density even further with Oracle Solaris and ZFS file system compression to achieve up to 15x compression of data without significant performance impact. Oracle Server X9-2L is also well suited for other storage-dense implementations, such as video compression and transcoding, which require a balanced combination of compute power and storage capacity at the same time.Oracle Server X9-2L ships with Oracle ILOM 5.0, a cloud-ready service processor designed for today's security challenges. Oracle ILOM provides real-time monitoring and management of all system and chassis functions as well as enables remote management of Oracle servers. Oracle ILOM uses advanced service processor hardware with built-in hardening and encryption as well as improved interfaces to reduce the attack surface and improve overall security. Oracle ILOM has improved firmware image validation through the use of improved firmware image signing. This mechanism provides silicon-anchored service processor firmware validation that cryptographically prevents malicious firmware from booting. After Oracle ILOM's boot code is validated by the hardware, a chain of trust allows each subsequent firmware component in the boot process to be validated. Finally, with a focus on security assurance, using secure coding and testing methodologies, Oracle is able to maximize firmware security by working to prevent and remediate vulnerabilities prior to release. With advanced system cooling that is unique to Oracle, Oracle Server X9-2L achieves system efficiencies that result in power savings and maximum uptime. Oracle Advanced System Cooling utilizes remote temperature sensors for fan speed control, minimizing power consumption while keeping optimal temperatures inside the server. These remote temperature sensors are designed into key areas of this server to ensure efficient fan usage by organizing all major subsystems into cooling zones. This technology helps reduce energy consumption in a way that other servers cannot.Oracle Premier Support customers have access to My Oracle Support and multi-server management tools in Oracle Enterprise Manager, a critical component that enables application-to-disk system management including servers, virtual Key ValueOracle Server X9-2L is the most storage-dense, versatile two-socket server in its class for the enterprise data center, packing the optimal balance of compute power, memory capacity, and I/O capacity into a compact and energy-efficient 2U enclosure. Related productsOracle Server X9-2Oracle Server X8-8Related servicesThe following services are available from Oracle Customer Support:SupportInstallationEco-optimization servicesmachines, databases, storage, and networking enterprise wide in a single pane of glass. Oracle Enterprise Manager enables Exadata, database, and systems administrators to proactively monitor the availability and health of their systems and to execute corrective actions without user intervention, enabling maximum service levels and simplified support.With industry-leading in-depth security spanning its entire portfolio of software and systems, Oracle believes that security must be built in at every layer of the IT environment. In order to build x86 servers with end-to-end security, Oracle maintains 100 percent in-house design, controls 100 percent of the supply chain, and controls 100 percent of the firmware source code. Oracle’s x86 servers enable only secure protocols out of the box to prevent unauthorized access at point of install. For even greater security, customers running Oracle Ksplice on Oracle’s x86 servers will benefit greatly from zero downtime patching of the Oracle Linux kernel.Oracle is driven to produce the most reliable and highest performing x86 systems in its class, with security-in-depth features layered into these servers, for two reasons: Oracle Cloud Infrastructure and Oracle Engineered Systems. At their foundation, these rapidly expanding cloud and converged infrastructure businesses run on Oracle’s x86 servers. To ensure that Oracle’s SaaS, PaaS, and IaaS offerings operate at the highest levels of efficiency, only enterprise-class features are designed into these systems, along with significant co-development among cloud, hardware, and software engineering. Judicious component selection, extensive integration, and robust real-world testing enable the optimal performance and reliability critical to these core businesses. All the same features and benefits available in Oracle’s cloud are standard in Oracle’s x86 standalone servers, helping customers to easily transition from on-premises applications to cloud with guaranteed compatibility and efficiency.Oracle Server X9-2L System SpecificationsCache•Level 1: 32 KB instruction and 32 KB data L1 cache per core•Level 2: 1 MB shared data and instruction L2 cache per core•Level 3: up to 1.375 MB shared inclusive L3 cache per coreMain Memory•Thirty-two DIMM slots provide up to 2 TB of DDR4 DIMM memory•RDIMM options: 32 GB or 64 GB at DDR4-3200 dual rankInterfaces Standard I/O•One 1000BASE-T network management Ethernet port•One 1000BASE-T host management Ethernet port•One RJ-45 serial management port•One rear USB 3.0 port•Expansion bus: 10 PCIe 4.0 slots, two x16 and eight x8 slots•Supports LP-PCIe cards including Ethernet, FC, SAS and flashStorage•Twelve 3.5-inch front hot-swappable disk bays plus two internal M.2boot drives•Disk bays can be populated with 3.5-inch 18 TB HDDs or 2.5-inch 6.8 or 3.84 NVMesolid-state drives (SSDs)•PCIe flash•Sixteen-port 12 Gb/sec RAID HBA supporting levels: 0, 1, 5, 6, 10, 50, and 60 with 1GB of DDR3 onboard memory with flash memory backup via SAS-3 HBA PCIe cardHigh-Bandwidth Flash•All flash configuration—up to 132.8 TB in the all-flash configuration (maximum of12 hot-swappable 6.8 TB NVMe SSDs and eight 6.4 TB NVMe PCIe cards)NVMe functionality in 3.5-inch disk bays 8-11 requires an Oracle NVMeretimer that is installed in PCIe slot 10Systems Management Interfaces•Dedicated 1000BASE-T network management Ethernet port (10/100/1000 Gb/sec)•One 1000BASE-T host management Ethernet port (10/100/1000 Gb/sec)•In-band, out-of-band, and side-band network management access•One RJ-45 serial management portService ProcessorOracle Integrated Lights Out Manager (Oracle ILOM) provides:•Remote keyboard, video, and mouse redirection•Full remote management through command-line, IPMI, and browser interfaces•Remote media capability (USB, DVD, CD, and ISO image)•Advanced power management and monitoring•Active Directory, LDAP, and RADIUS support•Dual Oracle ILOM flash•Direct virtual media redirection•FIPS 140-2 mode using OpenSSL FIPS certification (#1747)Monitoring•Comprehensive fault detection and notification•In-band, out-of-band, and side-band SNMP monitoring v2c and v3•Syslog and SMTP alerts•Automatic creation of a service request for key hardware faults with Oracleautomated service request (ASR)Oracle Enterprise Manager•Advanced monitoring and management of hardware and software•Deployment and provisioning of databases•Cloud and virtualization management•Inventory control and patch management•OS observability for performance monitoring and tuning•Single pane of glass for management of entire Oracle deployments, including onpremises and Oracle CloudSoftware Operating Systems•Oracle Linux•Oracle SolarisVirtualization•Oracle KVMFor more information on software go to: Oracle Server X9-2L Options & DownloadsOperating Environment •Ambient Operating temperature: 5°C to 40°C (41°F to 104°F)•Ambient Non-operating temperature: -40°C to 68°C (-40°F to 154°F)•Operating relative humidity: 10% to 90%, noncondensing•Non-operating relative humidity: up to 93%, noncondensing•Operating altitude: Maximum ambient operating temperature is derated by 1°C per 300 m of elevation beyond 900 m, up to a maximum altitude of 3000 m•Non-operating altitude: up to 39,370 feet (12,000 m)•Acoustic noise-Maximum condition: 7.1 Bels A weightedIdle condition: 7.0 Bels A weightedConnect with usCall +1.800.ORACLE1 or visit . Outside North America, find your local office at: /contact. /oracle /oracleCopyright © 2023, Oracle and/or its affiliates. All rights reserved. This document is provided for information purposes only, and the contents hereof are subject to change without notice. This document is not warranted to be error-free, nor subject to any other warranties or conditions, whether expressed orally or implied in law, including implied warranties and conditions of merchantability or fitness for a particular purpose. We specifically disclaim any liability with respect to this document, and no contractual obligations are formed either directly or indirectly by this document. This document may not be reproduced or transmitted in any form or by any means, electronic or mechanical, for any purpose, without our prior written permission.This device has not been authorized as required by the rules of the Federal Communications Commission. This device is not, and may not be, offered for sale or lease, or sold or leased, until authorization is obtained.Oracle and Java are registered trademarks of Oracle and/or its affiliates. Other names may be trademarks of their respective owners.Intel and Intel Xeon are trademarks or registered trademarks of Intel Corporation. All SPARC trademarks are used under license and are trademarks or registered trademarks of SPARC International, Inc. AMD, Opteron, the AMD logo, and the AMD Opteron logo are trademarks or registered trademarks of Advanced Micro Devices. UNIX is a registered trademark of The Open Group. 0120Disclaimer: If you are unsure whether your data sheet needs a disclaimer, read the revenue recognition policy. If you have further questions about your content and the disclaimer requirements, e-mail ********************.。
姿态估计算法汇总基于RGB、RGB-D以及点云数据作者:Tom Hardy点击上⽅“3D视觉⼯坊”,选择“星标”⼲货第⼀时间送达作者⼁Tom Hardy@知乎编辑⼁3D视觉⼯坊姿态估计算法汇总|基于RGB、RGB-D以及点云数据主要有整体⽅式、霍夫投票⽅式、Keypoint-based⽅式、Dense Correspondence⽅式等。
实现⽅法:传统⽅法、深度学习⽅式。
数据不同:RGB、RGB-D、点云数据等;标注⼯具实现⽅式不同整体⽅式整体⽅法直接估计给定图像中物体的三维位置和⽅向。
经典的基于模板的⽅法构造刚性模板并扫描图像以计算最佳匹配姿态。
这种⼿⼯制作的模板对集群场景不太可靠。
最近,⼈们提出了⼀些基于深度神经⽹络的⽅法来直接回归相机或物体的6D姿态。
然⽽,旋转空间的⾮线性使得数据驱动的DNN难以学习和推⼴。
1.Discriminative mixture-of-templates for viewpoint classification2.Gradient response maps for realtime detection of textureless objects.paring images using the hausdorff distance4.Implicit 3d orientation learning for 6d object detection from rgb images.5.Instance- and Category-level 6D Object Pose Estimation基于模型2.Deep model-based 6d pose refinement in rgbKeypoint-based⽅式⽬前基于关键点的⽅法⾸先检测图像中物体的⼆维关键点,然后利⽤PnP算法估计6D姿态。
1.Surf: Speeded up robust features.2.Object recognition from local scaleinvariant features3.3d object modeling and recognition using local affine-invariant image descriptors and multi-view spatial constraints.5.Stacked hourglass networks for human pose estimation6.Making deep heatmaps robust to partial occlusions for 3d object pose estimation.7.Bb8: A scalable, accurate, robust to partial occlusion method for predicting the 3d poses of challenging objects without using depth8.Real-time seamless single shot 6d object pose prediction.9.Discovery of latent 3d keypoints via end-toend geometric reasoning.10.Pvnet: Pixel-wise voting network for 6dof pose estimation.Dense Correspondence/霍夫投票⽅式1.Independent object class detection using 3d feature maps.2.Depth encoded hough voting for joint object detection and shape recovery.3.aware object detection and pose estimation.4.Learning 6d object pose estimation using 3d object coordinates.5.Global hypothesis generation for 6d object pose estimation.6.Deep learning of local rgb-d patches for 3d object detection and 6d pose estimation.7.Cdpn: Coordinates-based disentangled pose network for real-time rgb-based 6-dof object pose estimation.8.Pix2pose: Pixel-wise coordinate regression of objects for 6d pose estimation.9.Normalized object coordinate space for categorylevel 6d object pose and size estimation.10.Recovering 6d object pose and predicting next-bestview in the crowd.基于分割深度学习相关⽅法1.PoseCNN: A convolutional neural network for 6d object pose estimation in cluttered scenes.2.Render for cnn: Viewpoint estimation in images using cnns trained with rendered 3d model views.6.Robust 6D Object Pose Estimation in Cluttered Scenesusing Semantic Segmentation and Pose Regression Networks - Arul Selvam Periyasamy, Max Schwarz, and Sven Behnke. [[Paper]数据格式不同根据数据格式的不同,⼜可分为基于RGB、RGB-D、点云数据的识别算法。
露天矿边坡影像三维重建技术刘军;王鹤;李峰;刘小阳【摘要】针对边坡稳定性评价研究的需要,提出了一种基于影像的露天矿边坡全自动三维重建方法。
该方法利用普通数码相机获取露天矿边坡序列影像,基于运动恢复结构( SfM)和多视图立体视觉( MVS)算法,生成了稠密三维点云;通过构建不规则三角网和纹理映射,制作了边坡数字表面模型。
试验结果表明:重建模型可全面表达露天矿边坡整体形态和局部细节特征,为有效评价边坡稳定性提供了科学依据;该技术具有成本低、高效、全自动等特点,非常适合存在潜在隐患的露天矿边坡动态变形监测。
%To meet the demand of slope stability evaluation,a fully automated 3D reconstruction approach of open-pit slope from images was put forward. Open-pit slope sequence images were first collected with a consumer-grade camera. And then,dense 3D point clouds were generated by integrating structure from motion ( SfM) and multi-view stereo ( MVS) algo-rithms. Finally,high-resolution digital surface models of open-pit slope were made by constructing the triangular irregular net-work and texture mapping. The experiment showed that the overall form and local characteristics of open-pit slope can be accu-rately expressed through reconstructed model, which can provide powerful support for the correct analysis and evaluation of slope stability. The presented technology has the features of low cost,high efficiency,full automation,and it is especially suit-able for dynamic deformation monitoring of the open-pit slope in potential risk.【期刊名称】《金属矿山》【年(卷),期】2015(000)004【总页数】3页(P259-261)【关键词】露天矿;边坡;运动恢复结构;多视图立体视觉;三维重建【作者】刘军;王鹤;李峰;刘小阳【作者单位】防灾科技学院防灾工程系,河北燕郊101601;防灾科技学院地震科学系,河北燕郊101601;防灾科技学院防灾工程系,河北燕郊101601;防灾科技学院防灾工程系,河北燕郊101601【正文语种】中文【中图分类】TD672伴随露天采矿深度和边坡角度的不断增大,边坡的稳定性问题会给矿山的安全生产带来隐患。
GE HealthcareLife SciencesData file 29-0981-07 AA Imaging systems, software, and accessories Amersham™ Imager 600Amersham Imager 600 series is a new range of sensitiveand robust imagers for the capture and analysis of highresolution digital images of protein and DNA samples in gelsand membranes. These multipurpose imagers bring highperformance imaging to chemiluminescence, fluorescence,and colorimetric applications. The design of AmershamImager 600 combines our Western Blotting applicationexpertise with optimized CCD technology and exceptionaloptics from Fujifilm™. The system has an integrated analysissoftware and intuitive workflow, which you can operatefrom an iPad™ or alternative touch screen device, togenerate and analyze data quickly and easily.Amersham Imager 600 delivers:• Intuitive operation: You can operate the instrument froma tablet computer with an intuitive design and easy-to-useimage analysis software. You do not need prior imagerexperience or training to obtain high-quality results.Use the automatic capture mode for convenient exposure• Excellent performance:The system uses a super-honeycomb CCD and a large aperture f/0.85 FUJINON™ lens, which consistently delivers high-resolution images, high sensitivity, broad dynamic range (DR), and minimal cross-talk• Robustness:Combining minimal maintenance with our proven expertise in Western blotting and electrophoresis makes the imager well suited for multiuser laboratories. Amersham Imager 600 is an upgradable series of imagers that can grow with your imaging needs DescriptionAmersham Imager 600 series is equipped with a dark sample cabinet, a camera system, filter wheel, light sources, anda built-in computer with control and analysis software. Network connection and USB ports are standard (Fig 2). Fig 1. Amersham Imager 600 series is a range of robust and easy-to-use systems for chemiluminescent, colorimetric, and fluorescent image capture. Settings such as focus, filter, illuminator, and exposure type are automatically controlled by the integrated software. You would obtain high resolution images and precise quantitation of low signals with the multipurpose 16-bit3.2 megapixel camera fitted with a large aperture lens. The detector is cooled to reduce noise levels for high sensitivity and wide dynamic range. Rapid cooling leads to a short startup time, which makes the instrument ready to use in less than 5 min.You can place the sample tray at one of two different heights in the sample compartment to produce image-acquisitionareas of 220 × 160 mm and 110 × 80 mm, respectively.2 29-0981-07 AAThe system can be used for a wide range of applications and it is fully upgradable between four different configurations (Table 1). Each configuration can be used for chemiluminescent detection and nonquantitative gel documentation. The different configurations are equipped with light sources and filters for UV and white light trans-illumination, and red, green and blueTable 1. Amersham Imager 600 series comprises four different configurations. Amersham Imager 600 QC is designed for QC applicationsAmersham Imager 600Amersham Imager 600UVAmersham Imager 600RGB Amersham Imager 600QCWhite light (epi)××××Chemiluminescence ××××UV Fluorescence o ×××RGB Fluorescence o o ×o White light (trans) calibrated OD measurements oo××× StandardO OptionalFig 2. Amersham Imager 600 is ready to capture images within 5 min after startup. The imager is equipped with USB ports and a network connection.Network connectionUSB portsUSB ports TouchscreenPower switch Sample tray Cabinet doorepi-illumination for multiple fluorescence detection. Optical density (OD) measurements are calibrated for quantitation of colorimetric staining applications.You can operate the system via an integrated computer, controlled from a wireless iPad, a USB-connected touch screen, or a traditional monitor with a mouse and keyboard.Imaging performanceA bright, wide aperture FUJINON f/0.85 lens developed for chemiluminescent imaging projects sharp images onto a specially patterned CCD (Fig 3).Fig 3. The special octagonal interwoven pixel layout offers a dense matrix fora more efficient capture of light compared to a standard, square-pixel layout.Intuitive operation and analysisAmersham Imager 600 can be controlled from either an iPad or an alternative touch screen device. The user interface is intuitive and the workflow is easy to follow. The system is fully automated, which means that after startup, you do not need to perform focusing, insertion of light sources, changing of filters, calibrations, or other adjustments.When the system is in automatic image capture mode, it performs a short pre-exposure of the whole sample to determine the optimum exposure time for the strongest signal without saturating the image so that an accurate quantitation of the sample can be attained. In semi-automatic image capture mode, an automated exposureis made based on an area of interest defined by you. Exposure times are also easy to set manually.After image acquisition, the seamless workflow allows youto detect and quantitate bands, determine molecular weight, and perform normalization. The results are presented in both tabular and graphical formats so that you can easily and quickly analyze your data. For additional flexibility during data analysis, we offer ImageQuant™ TL software.You can use the system to obtain images of colorimetric markers and stains, such as Coomassie™ Blue or silver. Moreover, white light imaging can be combined with chemiluminescence and fluorescence imaging to generate overlay images of marker and sample. This feature allows quick molecular weight estimation and simplified documentation.The images can be stored in the system, on a USB memory stick or external hard drive, or in a network folder. Examples of imaging applicationsThe following examples of applications illustrate the performance and flexibility of Amersham Imager 600. Chemiluminescent Western blotting detection Quantitative Western blotting requires a signal response that is proportional to the amount of protein. A broad dynamic range with linear response allows you to simultaneously quantitate both high and low levels of proteins. The combination of Amersham Imager 600 with either Amersham ECL™ Prime or Amersham ECL Select™ resultsin a limit of detection in the picogram range and a dynamic range covering three orders of magnitude.Amersham Imager 600 has high sensitivity, which allowsyou to detect very weak signals in chemiluminescence andfluorescence applications for both protein and nucleic acids.Moreover, the wide dynamic range of the imagers—over fourorders of magnitude—allows weak and strong signals tobe quantitated accurately at the same time. The camera iscooled to -25°C to reduce dark noise giving less backgroundnoise during longer exposure times, which is especiallyimportant for the precise quantitation of very weak signalsin chemiluminescent Western blotting. The images areautomatically corrected for both geometric and intensitydistortion (radial, dark frame, and flat frame) in each imagingmode. This provides images that need minimal post-processingfor publication.RobustnessAmersham Imager 600 is a highly robust series of instruments,making it suitable for multi-user environments. The imagersdo not require calibration. Short exposure times and a fastanalysis workflow means that several researchers can use thesystem in the course of a day. The camera system is designedfor simple operation.29-0981-07 AA 34 29-0981-07 AAFig 6. The chemiluminescence mode allows simultaneous imaging of chemiluminescent samples and colored molecular weight markers. This image was taken from experiments for optimizing the expression of the protein DHFR in E. coli grown under different conditions.Fig 7. Evaluation of linearity, dynamic range, and limit of detection forfluorescence detection with Amersham Imager 600. A two-fold dilution series of phosphorylase b prelabeled with Cy5 shows a dynamic range of 3.3 orders of magnitude.Fluorescent imagingAmersham Imager 600 combined with Amersham ECL Plex™ provides high-quality data in applications that demand high sensitivity over a wide dynamic range. Furthermore, theminimal crosstalk of Amersham Imager 600, and the spectrally resolved dyes Cy™2, Cy3, and Cy5, makes it a suitable system for a wide range of multiplexing applications, such as the detection of several proteins at the same time or different proteins of similar size.Sample:E. coli lysateMembrane: Amersham Hybond ECL Blocking: 3% BSA in PBS-TMarker:Full range ECL Plex Fluorescent Rainbow Marker Primary antibody: Rabbit anti DHFR C-terminal 1:1000Secondary antibody: ECL Anti-rabbit IgG horseradish peroxidase 1:100 000Detection: Amersham ECL Select Imaging:Amersham Imager 600Imaging method: Chemiluminescence with colorimetric markerSample: Two-fold dilution series of LMW marker with Phophorylase bstarting at 200 ng Prelabeling: Cy5Imaging:Amersham Imager 600Imaging method: Fluorescence Cy5Limit of detection: 98 pg phosphorylase b Dynamic range:3.3 orders of magnitude123456789DHFR8.07.06.05.04.03.0Log protein amount (pg)L o g i n t e g r a t e d i n t e n s i t yPhophorylase b200 ng98 pgFig 5. Evaluation of limit of detection with Amersham Imager 600 forchemiluminescence using a two-fold dilution series of transferrin from 625 pg.8.07.06.05.04.03.0Log protein amount (pg)L o g i nt e g r a t e d in t en s i t yTransferrin625 pg2.5 pgSample : Two-fold dilution series of transferrin from 625 pg to 2.5 pg Membrane : Amersham Hybond P Blocking :3% BSA in PBS-TPrimary antibody : Rabbit anti-transferrin 1:1000Secondary antibody : ECL Anti-rabbit IgG horseradish peroxidase 1:75 000 Detection : Amersham ECL Select Imaging :Amersham Imager 600Imaging method :Chemiluminescence Limit of detection (LOD): 2.5 pg transferrinFig 4. A two-fold dilution series of NIH/3T3 cell lysate starting at 5 µg total protein was subjected to chemiluminescent Western blotting and ERK was detected with Amersham ECL Select. Dynamic range and linearity weredetermined. ERK could be detected in a cell lysate with 9.8 ng of total protein. Amersham Imager 600 showed a linear response for chemiluminescent detection with low noise, high sensitivity, and a wide dynamic range.8.07.57.06.56.05.55.04.54.0Log cell lysate amount (ng)L o g i n te g r a t e d i n t en si t yNIH/3T3 cell lysate5 µg9.8 ngSample: NIH/3T3 cell lysate two-fold dilution series starting at 5 µg Membrane : Amersham Hybond™ P Blocking : Amersham ECL Prime blocking agent 2% in PBS-T Primary antibody : Rabbit anti-ERK1/2 1:10 000Secondary antibody : ECL Anti-rabbit IgG horseradish peroxidase 1:100 000Detection : Amersham ECL Select Imaging : Amersham Imager 600Imaging method : Chemiluminescence Dynamic range: 2.7 orders of magnitude Amersham Imager 600 offers chemiluminescence imaging with an automatic overlay function. This allows simultaneous imaging of a chemiluminescent sample and a colored molecular weight marker. The overlay image retains the marker color.29-0981-07 AA 5Fig 8. Multiplex detection of total protein and target protein with Amersham ECL Plex and Amersham Imager 600. Detection of DHFR (Cy3 green) in nine different samples from a growth optimization of E. coli. Total protein in the samples was prelabeled with Cy5 (red). The overlay image shows the DHFR band in yellow. Crosstalk between Cy5 and Cy3 was minimal for Amersham Imager 600, which makes it suitable for multiplex applications.Fig 9. (A) Proteins stained with Coomassie Brilliant Blue and detected with Amersham Imager 600. The illustration shows nine different samples of E. coli lysates, from a growth optimization experiment for the expression of DHFR. Purified DHFR was used as a reference (sample 10). (B) Two-fold dilution series of the LMW-SDS Marker stained with SYPRO Ruby and detected with Amersham Imager 600.Sensitive imaging of total protein stainsProteins may be visualized by treating a gel with a total protein stain after performing 1D or 2D electrophoresis. The most commonly used stains are Coomassie Blue or silver staining. Fluorescent staining methods such as SYPRO™ Ruby protein gel stain have the advantage of being more sensitive.Sample: E. coli lysates Blocking:3% BSA in PBS-TPrimary antibody: Rabbit anti DHFR C-terminal 1:1000Secondary antibody: ECL Plex Goat anti rabbit-Cy3 IgG 1:2500Imaging:Amersham Imager 600Imaging method: Fluorescence Cy3, Cy5Sample: E. coli lysatesMarker:Full range ECL Plex Fluorescent Rainbow Marker Post staining: Coomassie Brilliant Blue Imaging:Amersham Imager 600Imaging method:Colorimetiric, white light epi-illuminationSample: Two fold dilution seires of LMW markerstarting at 1000 ng Post staining: Sypro RubyImaging:Amersham Imager 600Imaging method: Fluorescence Blue Epi excitation Limit of detection: 2 ng of carbonic anhydrase123456789Prelabeling Cy5Total proteinWB: DHFR Cy3Overlay12345678910Carbonic anhydraseLMW 1000 ng2 ng(A)(B)6 29-0981-07 AAFig 11. Image of a two-fold dilution series of LMW-SDS Marker in a gel stained with Coomassie Brilliant Blue. The image was recorded on Amersham Imager 600 in trans-illumination mode, which allows you to measure the optical density of protein bands without calibration.Sample:Two fold dilution series of LMW marker Post staining: Coomassie Brilliant Blue Imaging:Amersham Imager 600Imaging method: Colorimetric white transillumination Limit of detection: 16 ng of carbonic anhydrase Dynamic range:1.8 orders of magnitudeLog amount of carbonic anhydrase (ng)L o g i n t e g r a t e d i n t e n s i t yLMW markers1000 ngDNA imagingElectrophoretic separation of DNA is a common technique that is typically used for the analysis of vector cleavages, DNA purification, and verification of successful PCR. Traditionally, ethidium bromide (EtBr) has been used for visualizing DNA, but today there are many alternative DNA stains available, such as SYBR™ Green.Fig 10.Three-fold dilution series of KiloBase DNA Marker in agarose gel stained with SYBR Green and detected with Amersham Imager 600.Sample:Three-fold dilution series of KiloBase DNA Marker Post staining: Sybr Green I nucleic acid gel stain Imaging:Amersham Imager 600Imaging method: Fluorescence Cy2Limit of detection: 0.3 ng of total DNA Dynamic range:2.9 orders of magnitude8.07.57.06.56.05.55.04.54.0Log amount of total DNA (pg)L o g i n t e g r a t e d i n t e n s i t yKiloBase DNA Marker 250 ngQuantitative OD measurementAmersham Imager 600 QC is a dedicated configuration for densitometry applications in a QC environment. The system contributes to a reliable control of products because it is equipped with highly sensitive optics that can detect trace amounts of impurities accurately. Amersham Imager 600 QC is available with IQ/OQ and validation support.Amersham Imager 600 is autocalibrated for accurate and reliable measurements of optical density of proteins stained with colorimetric stains such as Coomassie or silver.Installation and Operational Qualification (IQ/OQ) validation servicesGE Healthcare offers validation services to support your equipment throughout its entire life cycle. Our validation tests and protocols are developed and approved byvalidation experts and performed by trained and certified service engineers. Our approach is in alignment with GAMP5, ICH Q8-10 and ASTM E2500, whereby validation activities and documentation focus on what is critical for end-product quality, and are scaled according to risk, complexity, and novelty. Our validation offering includes Installation and Operational Qualification (IQ/OQ), Requalification, and Change Control Protocols (CCP).29-0981-07 AA 7Ordering informationProductCode number Amersham Imager 60029-0834-61Amersham Imager 600UV 29-0834-63Amersham Imager 600QC 29-0834-64Amersham Imager 600RGB29-0834-67Accessories included (depending on configuration)Black tray AI60029-0834-17UV trans tray AI60029-0834-19White trans Tray AI60029-0834-18White Insert AI60029-0880-60Diffuser Board AI60029-0834-20Additional Accessories Gel sheets (for UV trans tray)29-0834-57Apple iPad 2 Wi-Fi –16GB –Black 29-0938-27Touch Screen Monitor with Stand 29-0939-66RangeBooster N USB Adapter 29-0928-76Additional SoftwareImageQuant TL 8.1, node locked license*29-0007-37ImageQuant TL 8.1, 5 x 1 node locked license*29-0008-10* External computer needed. Cannot be installed on Amersham Imager 600.Upgrade options Part/DescriptionRelevant for configurationCode number AI600 Upgrade 600 to 600 UV 60029-0834-22AI600 Upgrade 600 UV to 600 QC 600 UV 29-0834-24 AI600 Upgrade 600 QC to 600 RGB 600 QC 29-0834-25 AI600 Upgrade 600 UV to 600 RGB 600 UV29-0834-26IQ/OQ Validation service Amersham Imager 600 IQ/OQ29-0983-45Technical featuresTable 2. Amersham Imager 600 RGB specificationsCCD model:Peltier cooled Fujifilm Super CCD Pixel area 15.6 × 23.4 mmLens model:FUJINON Lens f/0.85 43 mm Cooling:Two-stage thermoelectric module with air circulation CCD Operating temperature -25°C Cooling down time:< 5 minDynamic range:16-bit, 4.8 orders of magnitude CCD resolution:2048 × 1472, 3.2 MpixelImage resolution:Maximum 2816 × 2048, 5.8 Mpixel Operation:Fully automated (auto exposure, no focus or other adjustment or calibration needed)Capture modes:Automatic, semi-automatic, manual (normal/incremental)Exposure time:1/10 s to 1 hourPixel correction:Dark frame correction, flatframe correction, and distortion correctionImage output:Gray scale 16 bit tif, Color image jpg, Gray scale jpg Sample size:160 × 220 mm Light sources:Blue Epi light: 460 nm Green Epi light: 520 nm Red Epi light: 630 nmUV transillumination light: 312 nm White light: 470 to 635 nmEmission filters:Cy2: 525BP20Cy3/EtBr: 605BP40Cy5: 705BP40Interface:USB 2.0 and Ethernet port Dimensions (W × H × D): 360 × 785 × 485 mmWeight: 43.6 kg (Amersham Imager 600 RGB)Input voltage:100 to 240 V Voltage variation:±10%Frequency:50/60 Hz Max power:250 W Operating temperature:18°C to 28°CHumidity:20% to 70% (no dew condensation)imagination at work GE, imagination at work, and GE monogram are trademarks of General Electric Company.Amersham, Cy, Hybond, ImageQuant, ECL, and ECL Select are trademarks of GE Healthcare companies.Coomassie is a trademark of Imperial Chemical Industries, Ltd. Fujifilm and Fujinon are trademarks of Fujifilm Corporation. iPad is a trademark of Apple Inc. SYBR is a trademark of Life Technologies Corporation. SYPRO isa trademark of Life Technologies Corp.CyDye: This product is manufactured under an exclusive license from Carnegie Mellon University and is covered by US patent numbers 5,569,587 and 5,627,027.The purchase of CyDye products includes a limited license to use the CyDye products for internal research and development but not for any commercial purposes. A license to use the CyDye products for commercial purposes is subject to a separate license agreement with GE Healthcare. Commercial use shall include:1. Sale, lease, license or other transfer of the material or any material derived or produced from it.2. Sale, lease, license or other grant of rights to use this material or any material derived or produced from it.3. Use of this material to perform services for a fee for third parties, including contract research and drug screening. If you require a commercial license to use this material and do not have one, return this material unopened toGE Healthcare Bio-Sciences AB, Bjorkgatan 30, SE-751 84 Uppsala, Sweden and any money paid for the material will be refunded.GE Healthcare UK LimitedAmersham PlaceLittle ChalfontBuckinghamshire, HP7 9NAUKGE Healthcare Europe, GmbHMunzinger Strasse 5D-79111 FreiburgGermanyGE Healthcare Bio-Sciences Corp.800 Centennial Avenue, P.O. Box 1327Piscataway, NJ 08855-1327USAGE Healthcare Japan CorporationSanken Bldg., 3-25-1, HyakuninchoShinjuku-ku, Tokyo 169-0073Japan29-0981-07 AA 02/2014For local office contact information, visit /contact /imagingGE Healthcare UK LimitedAmersham PlaceLittle ChalfontBuckinghamshire HP7 9NAUnited Kingdom。
北京大学学报(自然科学版) 第60卷 第1期 2024年1月Acta Scientiarum Naturalium Universitatis Pekinensis, Vol. 60, No. 1 (Jan. 2024)doi: 10.13209/j.0479-8023.2023.072一种消减多模态偏见的鲁棒视觉问答方法张丰硕李豫李向前†徐金安陈钰枫北京交通大学计算机与信息技术学院, 北京 100044; †通信作者摘要为了增强视觉问答模型的鲁棒性, 提出一种偏见消减方法, 并在此基础上探究语言与视觉信息对偏见的影响。
进一步地, 构造两个偏见学习分支来分别捕获语言偏见以及语言和图片共同导致的偏见, 利用偏见消减方法, 得到鲁棒性更强的预测结果。
最后, 依据标准视觉问答与偏见分支之间的预测概率差异, 对样本进行动态赋权, 使模型针对不同偏见程度的样本动态地调节学习程度。
在VQA-CP v2.0等数据集上的实验结果证明了所提方法的有效性, 缓解了偏见对模型的影响。
关键词视觉问答; 数据集偏差; 语言偏见; 深度学习Reducing Multi-model Biases for Robust Visual Question AnsweringZHANG Fengshuo, LI Yu, LI Xiangqian†, XU Jin’an, CHEN YufengSchool of Computer and Information Technology, Beijing Jiaotong University, Beijing 100044;Abstract In order to enhance the robustness of the visual question answering model, a bias reduction method is proposed. Based on this, the influence of language and visual information on bias effect is explored. Furthermore, two bias learning branches are constructed to capture the language bias, and the bias caused by both language and images. Then, more robust prediction results are obtained by using the bias reduction method. Finally, based on the difference in prediction probabilities between standard visual question answering and bias branches, samples are dynamically weighted, allowing the model to adjust learning levels for samples with different levels of bias.Experiments on VQA-CP v2.0 and other data sets demonstrate the effectiveness of the proposed method and alleviate the influence of bias on the model.Key words visual question answering; dataset bias; language bias; deep learning视觉问答(visual question answering, VQA)[1]是一项结合计算机视觉与自然语言处理的多模态任务, 其目标是根据图片来回答问题。
Accurate,Dense,and Robust Multi-View StereopsisYasutaka Furukawa1Department of Computer Scienceand Beckman InstituteUniversity of Illinois at Urbana-Champaign,USA1Jean Ponce1,2Willow Team–ENS/INRIA/ENPC D´e partement d’Informatique Ecole Normale Sup´e rieure,Paris,France2Abstract:This paper proposes a novel algorithm for calibrated multi-view stereopsis that outputs a(quasi)dense set of rectan-gular patches covering the surfaces visible in the input images. This algorithm does not require any initialization in the form of a bounding volume,and it detects and discards automatically out-liers and obstacles.It does not perform any smoothing across nearby features,yet is currently the top performer in terms of both coverage and accuracy for four of the six benchmarkdatasets pre-sented in[20].The keys to its performance are effective tech-niques for enforcing local photometric consistency and global visibility constraints.Stereopsis is implemented as a match,ex-pand,andfilter procedure,starting from a sparse set of matched keypoints,and repeatedly expanding these to nearby pixel corre-spondences before using visibility constraints tofilter away false matches.A simple but effective method for turning the resulting patch model into a mesh appropriate for image-based modeling is also presented.The proposed approach is demonstrated on vari-ous datasets including objects withfine surface details,deep con-cavities,and thin structures,outdoor scenes observed from a re-stricted set of viewpoints,and“crowded”scenes where moving obstacles appear in different places in multiple images of a static structure of interest.1.IntroductionAs in the binocular case,although most early work in multi-view stereopsis(e.g.,[12,15,19])tended to match and reconstruct all scene points independently,recent ap-proaches typically cast this problem as a variational one, where the objective is tofind the surface minimizinga global photometric discrepancy functional,regularized by explicit smoothness constraints[1,8,17,18,22,23](a g e-ometric consistency terms is sometimes added as well[3, 4,7,9]).Competingapproaches mostly differ in the type of optimization techniques that they use,ranging from local methods such as gradient descent[3,4,7],level sets[1,9,18],or expectation maximization[21],to global ones such as graph cuts[3,8,17,22,23].The variational approach has led to impressive progress,and several of the methods recently surveyed by Seitz et al.[20]achieve a rel-ative accuracy better than1/200(1mm for a20cm wide ob-ject)from a set of low-resolution(640×480)images.How-ever,it typically requires determininga boundingvolume (valid depth range,bounding box,or visual hull)prior to initiatingthe optimization process,which may not be feasi-ble for outdoor scenes and/or cluttered images.1We pro-pose instead a simple and efficient algorithm for calibrated multi-view stereopsis that does not require any initializa-tion,is capable of detectingand discardingoutliers and ob-stacles,and outputs a(quasi)dense collection of small ori-ented rectangular patches[6,13],obtained from pixel-level correspondences and tightly covering the observed surfaces except in small textureless or occluded regions.It does not perform any smoothingacross nearby features,yet is cur-rently the top performer in terms of both coverage and accu-racy for four of the six benchmark datasets provided in[20].The keys to its performance are effective techniques for en-forcinglocal photometric consistency and g lobal visibility constraints.Stereopsis is implemented as a match,expand, andfilter procedure,startingfrom a sparse set of matched keypoints,and repeatedly expandingthese to nearby pixel correspondences before usingvisibility constraints tofil-ter away false matches.A simple but effective method for turningthe resultingpatch model into a mesh suitable for image-based modeling is also presented.The proposed ap-proach is applied to three classes of datasets:•objects,where a single,compact object is usually fully visible in a set of uncluttered images taken from all around it,and it is relatively straightforward to extract the apparent contours of the object and compute its visual hull;•scenes,where the target object(s)may be partially oc-cluded and/or embedded in clutter,and the range of view-points may be severely limited,preventingthe computation of effective boundingvolumes(typical examples are out-door scenes with buildings or walls);and1In addition,variational approaches typically involve massive opti-mization tasks with tens of thousands of coupled variables,potentially limitingthe resolution of the correspondingreconstructions(see,however,[18]for a fast GPU implementation).We will revisit tradeoffs betweencomputational efficiency and reconstruction accuracy in Sect.5.1Figure1.Overall approach.From left to right:a sample input image;detected features;reconstructed patches after the initial matching;final patches after expansion andfiltering;polygonal surface extracted from reconstructed patches.•crowded scenes,where movingobstacles appear in differ-ent places in multiple images of a static structure of interest(e.g.,people passing in front of a building).Techniques such as space carving[12,15,19]and vari-ational methods based on gradient descent[3,4,7],levelsets[1,9,18],or graph cuts[3,8,17,22,23]typicallyrequire an initial boundingvolume and/or a wide rang e ofviewpoints.Object datasets are the ideal input for these al-gorithms,but methods using multiple depth maps[5,21]orsmall,independent surface elements[6,13]are better suitedto the more challenging scene datasets.Crowded scenesare even more difficult.The method proposed in[21]usesexpectation maximization and multiple depth maps to re-construct a crowded scene despite the presence of occlud-ers,but it is limited to a small number of images(typi-cally three).As shown by qualitative and quantitative ex-periments in the rest of this paper,our algorithm effec-tively handles all three types of data,and,in particular,outputs accurate object and scene models withfine surfacedetail despite low-texture regions,large concavities,and/orthin,high-curvature parts.As noted earlier,it implementsmulti-view stereopsis as a simple match,expand,andfil-ter procedure(Fig.1):(1)Matching:features found byHarris and Difference-of-Gaussians operators are matchedacross multiple pictures,yieldinga sparse set of patchesassociated with salient image regions.Given these initialmatches,the followingtwo steps are repeated n times(n=3in all our experiments):(2)Expansion:a technique similarto[16,2,11,13]is used to spread the initial matches tonearby pixels and obtain a dense set of patches.(3)Fil-tering:visibility constraints are used to eliminate incorrectmatches lyingeither in front or behind the observed surface.This approach is similar to the method proposed by Lhuil-lier and Quan[13],but their expansion procedure is greedy,while our algorithm iterates between expansion andfilter-ingsteps,which allows us to process complicated surfaces.Furthermore,outliers cannot be handled in their method.These differences are also true with the approach by Kushaland Ponce[11]in comparison to ours.In addition,only apair of images can be handled at once in[11],while ourmethod can process arbitrary number of images uniformly.2.Key Elements of the Proposed ApproachBefore detailingour alg orithm in Sect.3,we define herethe patches that will make up our reconstructions,as wellas the data structures used throughout to represent the inputimages.We also introduce two other fundamental buildingblocks of our approach,namely,the methods used to ac-curately reconstruct a patch once the correspondingimag efragments have been matched,and determine its visibility.2.1.Patch ModelsA patch p is a rectangle with center c(p)and unit nor-mal vector n(p)oriented toward the cameras observingit(Fig.2).We associate with p a reference image R(p),cho-sen so that its retinal plane is close to parallel to p with littledistortion.In turn,R(p)determines the orientation and ex-tent of the rectangle p in the plane orthogonal to n(p),sothe projection of one of its edges into R(p)is parallel to theimage rows,and the smallest axis-aligned square contain-ingits imag e covers aµ×µpixel2area(we use values of5or7forµin all of our experiments).Two sets of pic-tures are also attached to each patch p:the images S(p)where p should be visible(despite self-occlusion),but mayin practice not be recognizable(due to highlights,motionblur,etc.),or hidden by movingobstacles,and the imag esT(p)where it is truly found(R(p)is of course an elementof T(p)).We enforce the followingtwo constraints on themodel:First,we enforce local photometric consistency byrequiringthat the projected textures of every patch p be con-sistent in at leastγimages(in other words|T(p)|≥γ,withγ=3in all but three of our experiments,whereγis set to2).Second,we enforce global visibility consistency by re-quiringthat no patch p be occluded by any other patch inany image in S(p).22.2.Image ModelsWe associate with each image I a regular grid ofβ1×β1pixel2cells C(i,j),and attempt to reconstruct at least one2A patch p may be occluded in one or several of the images in S(p)by movingobstacles,but these are not reconstructed by our algorithm andthus do not generate occluding patches.Figure2.Definition of a patch(left)and of the images associated with it(right).See text for the details.patch in every cell(we use values of1or2forβ1in all our experiments).The cell C(i,j)keeps track of two dif-ferent sets Q t(i,j)and Q f(i,j)of reconstructed patches po-tentially visible in C(i,j):A patch p is stored in Q t(i,j)ifI∈T(p),and in Q f(i,j)if I∈S(p)\T(p).We also as-sociate with C(i,j)the depth of the center of the patch in Q t(i,j)closest to the optical center of the corresponding camera.This amounts to attachinga depth map to I,which will prove useful in the visibility calculations of Sect.2.4.2.3.Enforcing Photometric ConsistencyGiven a patch p,we use the normalized cross correlation (NCC)N(p,I,J)of its projections into the images I and J to measure their photometric consistency.Concretely,aµ×µgrid is overlaid on p and projected into the two images,the correlated values beingobtained throug h bilinear interpo-lation.Given a patch p,its reference image R(p),and the set of images T(p)where it is truly visible,we can now estimate its position c(p)and its surface normal n(p)by maximizingthe averag e NCC score¯N(p)=1∑I∈T(p),I=R(p)N(p,R(p),I)(1)with respect to these unknowns.To simplify computations, we constrain c(p)to lie on the ray joiningthe optical center of the reference camera to the correspondingimag e point, reducingthe number of deg rees of freedom of this opti-mization problem to three—depth alongthe ray plus yaw and pitch angles for n(p),and use a conjugate gradient method[14]tofind the optimal parameters.Simple meth-ods for computingreasonable initial g uesses for c(p)and n(p)are given in Sects.3.1and3.2.2.4.Enforcing Visibility ConsistencyThe visibility of each patch p is determined by the im-ages S(p)and T(p)where it is(potentially or truly)ob-served.We use two slightly different methods for construct-ing S(p)and T(p)dependingon the stag e of our reconstruc-tion algorithm.In the matching phase(Sect.3.1),patches are reconstructed from sparse feature matches,and we have to rely on photometric consistency constraints to deter-mine(or rather obtain an initial guess for)visibility.Con-cretely,we initialize both sets of images as those for whichthe NCC score exceeds some threshold:S(p)=T(p)= {I|N(p,R(p),I)>α0}.On the other hand,in the expan-sion phase of our algorithm(Sect.3.2),patches are by con-struction dense enough to associate depth maps with all im-ages,and S(p)is constructed for each patch by thresholding these depth maps—that is,S(p)={I|d I(p)≤d I(i,j)+ρ1}, where d I(p)is the depth of the center of p alongthe corre-spondingray of imag e I,and d I(i,j)is the depth recorded in the cell C(i,j)associated with image I and patch p.The value ofρ1is determined automatically as the distance at the depth of c(p)correspondingto an imag e displacement ofβ1pixels in R(p).Once S(p)has been estimated,photo-metric consistency is used to determine the images where p is truly observed as T(p)={I∈S(p)|N(p,R(p),I)>α1}. This process may fail when the reference image R(p)is it-self an outlier,but,as explained in the next section,our al-gorithm is designed to handle this problem.Iterating its matchingandfilteringsteps also helps improve the reliabil-ity and consistency of the visibility information.3.Algorithm3.1.MatchingAs thefirst step of our algorithm,we detect corner and blob features in each image using the Harris and Difference-of-Gaussian(DoG)operators.3To ensure uniform cov-erage,we lay over each image a coarse regular grid of β2×β2pixel2cells,and return as corners and blobs for each cell theηlocal maxima of the two operators with strongest responses(we useβ2=32andη=4in all our experi-ments).After these features have been found in each image, they are matched across multiple pictures to reconstruct a sparse set of patches,which are then stored in the grid of cells C(i,j)overlaid on each image(Fig.3):Consider an image I and denote by O the optical center of the corre-spondingcamera.For each feature f detected in I,we col-lect in the other images the set F of features f of the same type(Harris or DoG)that lie withinι=2pixels from the correspondingepipolar lines,and triang ulate the3D points associated with the pairs(f,f ).We then consider these points in order of increasingdistance from O as potential patch centers,4and return thefirst patch“photoconsistent”in at leastγimages(Fig.3,top).More concretely,for each 3Briefly,let us denote by Gσa2D Gaussian with standard deviation σ.The response of the Harrisfilter at some image point is defined as H=det(M)−λtrace2(M),where M=Gσ0∗(∇I∇I T),and∇I is computedby convolvingthe imag e I with the partial derivatives of the Gaussian Gσ1. The response of the DoGfilter is D=|(Gσ2−G√2σ2)∗I|.We useσ0=σ1=σ2=1pixel andλ=0.06in all of our experiments.4Empirically,this heuristic has proven to be effective in selecting mostly correct matches at a modest computational expense.I 1I 3f F ={ , , , }Epipolar line I 2Detected features(Harris/DoG)//Features satisfying epipolar consistency (Harris/DoG)Input:Features detected in each image.Output:Initial sparse set of patches P .Cover each image with a grid of β1×β1pixel 2cells;P ←φ;For each image I with optical center OFor each feature f detected in I and lyingin an empty cell F ←{Features satisfyingthe epipolar consistency };Sort F in an increasingorder of distance from O ;For each feature f ∈FR (p )←I ;T (p )←{J |N (p ,R (p ),J )≥α0};c (p )←3D point triangulated from f and f ;n (p )←Direction of optical ray from c (p )to O ;n (p ),c (p )←argmax ¯N(p );S (p )←{J |N (p ,R (p ),J )≥α0};T (p )←{J |N (p ,R (p ),J )≥α1};If |T (p )|≥γregister p to the correspondingcells in S (p );exit innermost For loop,and add p to P .Figure 3.Feature matching algorithm.Top:An example showingthe features f ∈F satisfyingthe epipolar constraint in images I 2and I 3as they are matched to feature f in image I 1(this is an illustration only,not showingactual detected features).Bottom:The matchingalg orithm.The values used for α0and α1in all our experiments are 0.4and 0.7respectively.feature f ,we construct the potential surface patch p by tri-angulating f and f to obtain an estimate of c (p ),assign to n (p )the direction of the optical ray joiningthis point to O ,and set R (p )=I .After initializing T (p )by usingphoto-metric consistency as in Sect.2.4,we use the optimization process described in Sect.2.3to refine the parameters of c (p )and n (p ),then initialize S (p )and recompute T (p ).Fi-nally,if p satisfies the constraint |T (p )|≥γ,we compute its projections in all images in S (p ),register it to the corre-spondingcells,and add it to P (Fig.3,bottom).Note that since the purpose of this step is only to reconstruct an initial,sparse set of patches,features lyingin non-empty cells are skipped for efficiency.Also note that the patch generation process may fail if the reference image R (p )is an outlier,for example when f correspond to a highlight.This does not prevent,however,the reconstruction of the correspond-ingsurface patch from another imag e.The second part of our algorithm iterates (three times in all our experiments)between an expansion step to obtain dense patches and a filteringstep to remove erroneous matches and enforce vis-ibility consistency,as detailed in the next two sections.3.2.ExpansionAt this stage,we iteratively add new neighbors to ex-istingpatches until they cover the surfaces visible in the scene.Intuitively,two patches p and p are considered to be neighbors when they are stored in adjacent cells C (i ,j )and C (i ,j )of the same image I in S (p ),and their tangent planes are close to each other.We only attempt to create new neighbors when necessary—that is,when Q t (i ,j )is empty,5and none of the elements of Q f (i ,j )is n-adjacent to p ,where two patches p and p are said to be n-adjacent when |(c (p )−c (p ))·n (p )|+|(c (p )−c (p ))·n (p )|<2ρ2.Similar to ρ1,ρ2is determined automatically as the distance at the depth of the mid-point of c (p )and c (p )correspond-ingto an imag e displacement of β1pixels in R (p ).When these two conditions are verified,we initialize the patch p by assigning to R (p ),T (p ),and n (p )the corresponding values for p ,and assigning to c (p )the point where the viewingray passingthroug h the center of C (i ,j )intersects the plane containingthe patch p .Next,c (p )and n (p )are refined by the optimization procedure discussed in Sect.2.3,and S (p )is initialized from the depth maps as explained in Sect.2.4.Since some matches (and thus the correspond-ingdepth map information)may be incorrect at this point,the elements of T (p )are added to S (p )to avoid missing any image where p may be visible.Finally,after updating T (p )usingphotometric constraints as in Sect.2.4,we ac-cept the patch p if |T (p )|≥γstill holds,then register it to Q t (i ,j )and Q f (i ,j ),and update the depth maps associ-ated with images in S (p ).See Fig .4for the algorithm.3.3.FilteringTwo filteringsteps are applied to the reconstructed patches to further enforce visibility consistency and remove erroneous matches.The first filter focuses on removing patches that lie outside the real surface (Fig.5,left):Con-sider a patch p 0and denote by U the set of patches that it oc-cludes.We remove p 0as an outlier when |T (p 0)|¯N(p 0)<∑p j ∈U ¯N(p j )(intuitively,when p 0is an outlier,both ¯N (p 0)and |T (p 0)|are expected to be small,and p 0is likely to be removed).The second filter focuses on outliers lyingin-side the actual surface (Fig.5,right):We simply recompute S (p 0)and T (p 0)for each patch p 0usingthe depth maps associated with the correspondingimag es (Sect.2.4),and5Intuitively,any patch p in Q t (i ,j )would either already be a neigh-bor of p ,or be separated from it by a depth discontinuity,neither case warrantingthe addition of a new neig hbor.Input:Patches P from the feature matchingstep.Output:Expanded set of reconstructed patches.Use P to initialize,for each image,Q f ,Q t ,and its depth map.While P is not emptyPick and remove a patch p from P ;For each image I ∈T (p )and cell C (i ,j )that p projects onto For each cell C (i ,j )adjacent to C (i ,j )such that Q t (i ,j )is empty and p is not n-adjacent to any patch in Q f(i ,j )Create anew p ,copying R (p ),T (p )and n (p )from p ;c (p )←Intersection of optical ray throughcenter of C (i ,j )with plane of p ;n (p ),c (p )←argmax ¯N(p );S (p )←{Visible images of p estimated by thecurrent depth maps }∪T (p );T (p )←{J ∈S (p )|N (p ,R (p ),J )≥α1};If |T (p )<γ|,go back to For -loop;Add p to P ;Update Q t ,Q f and depth maps for S (p );Return all the reconstructed patches stored in Q f and Q t .Figure 4.Patch expansion algorithm.IFigure 5.Outliers lying outside (left)or inside (right)the correct surface.Arrows are drawn between the patches p i and the images I j in S (p i ),while solid arrows correspond to the case where I j ∈T (p i ).U denotes a set of patches occluded by an outlier.See text for details.remove p 0when |T (p 0)|<γ.Note that the recomputed values of S (p 0)and T (p 0)may be different from those ob-tained in the expansion step since more patches have been computed after the reconstruction of p 0.Finally,we enforce a weak form of regularization as follows:For each patch p ,we collect the patches lyingin its own and adjacent cells in all images of S (p ).If the proportion of patches that are n-adjacent to p in this set is lower than ε=0.25,p is removed as an outlier.The threshold α1is initialized with 0.7,and lowered by 0.2after each expansion/filteringiteration.4.Polygonal Surface ReconstructionThe reconstructed patches form an oriented point ,or sur-fel model.Despite the growing popularity of this type of models in the computer graphics community [10],it re-mains desirable to turn our collection of patches into sur-face meshes for image-based modeling applications.TheS*S n (v )ΠFigure 6.Polygonal surface reconstruction.Left:bounding vol-umes for the dino (visual hull),steps (convex hull),and city-hall (union of hemispheres)datasets featured in Figs.7,9and 10.Right:geometric elements driving the deformation process.approach that we have adopted is a variant of the iterative deformation algorithm presented in [4],and consists of two phases.Briefly,after initializinga polyg onal surface from a predetermined boundingvolume,the convex hull of the reconstructed points,or a set of small hemispheres cen-tered at these points and pointingaway from the cameras,we repeatedly move each vertex v accordingto three forces (Fig.6):a smoothness term for regularization;a photomet-ric consistency term,which is based on the reconstructed patches in the first phase,but is computed solely from the mesh in the second phase;and,when accurate silhouettes are available,a rim consistency term pullingthe rim of the deformingsurface toward the correspondingvisual cones.Concretely,the smoothness term is −ζ1∆v +ζ2∆2v ,where ∆denotes the (discrete)Laplacian operator relative to a local parameterization of the tangent plane in v (ζ1=0.6and ζ2=0.4are used in all our experiments).In the first phase,the photometric consistency term for each vertex v essentially drives the surface towards reconstructed patches and is given by ν(v )n (v ),where n (v )is the inward unit normal to S in v ,ν(v )=max (−τ,min (τ,d (v ))),and d (v )is the signed distance between v and the true surface S ∗along n (v )(the parameter τis used to bound the magni-tude of the force,ensure stable deformation and avoid self-intersections;its value is fixed as 0.2times the average edge length in S ).In turn,d (v )is estimated as follows:We col-lect the set Π(v )of π=10patches p with (outward)nor-mals compatible with that of v (that is,−n (p )·n (v )>0,see Fig.6)that lie closest to the line defined by v and n (v ),and compute d (v )as the weighted average distance from v to the centers of the patches in Π(v )along n (v )—that is,d (v )=∑p ∈Π(v )w (p )[n (v )·(c (p )−v )],where the weights w (p )are Gaussian functions of the distance between c (p )and the line,with standard deviation ρ1defined as before,and normalized to sum to 1.In the second phase,the pho-tometric consistency term is computed for each vertex by usingthe patch optimization routine as follows.At each vertex v ,we create a patch p by initializing c (p )with v ,n (p )with a surface normal estimated at v on S ,and a set of visible images S (p )from a depth-map testingon the mesh S at v ,then apply the patch optimization routine described in Sect.2.3.Let c ∗(p )denote the value of c (p )after the optimization,then c ∗(p )−c (p )is used as the photometric consistency term.In the first phase,we iterate until conver-Table1.Characteristics of the datasets used in our experiments. roman and skull datasets have been acquired in our lab,while other datasets have been kindly provided by S.Seitz,B.Curless, J.Diebel,D.Scharstein,and R.Szeliski(temple and dino,see also[20]);C.Hern´a ndez Esteban,F.Schmitt and the Museum of Cherbourg(polynesian);S.Sullivan and Industrial Light and Magic(face,face-2,body,steps,and wall);and C.Strecha(city-hall and brussels).Name Images Image Sizeβ1µγroman481800×1200153temple16640×480153dino16640×480173skull242000×2000253 polynesian361700×2100253 face41400×2200172face-2131500×1400153body41400×2200172steps71500×1400173city-hall53000×2000273wall91500×1400173brussels32000×1300152gence,remesh,increase the resolution of the surface,and repeat the process until the desired resolution is obtained (in particular,until image projections of edges of the mesh become approximatelyβ1pixels in length,see[4]for de-tails).The second phase is applied to the mesh only in its desired resolution as afinal refinement.5.Experiments and DiscussionWe have implemented the proposed approach in C++, usingthe WNLI B[14]implementation of conjugate gradi-ent in the patch optimization routine.The datasets used in our experiments are listed in Table1,together with the num-ber of input images,their approximate size and a choice of parameters for each data set.Note that all the parameters except forβ1,µandγhave beenfixed in our experiments.We havefirst tested our algorithm on object datasets (Figs.1and7)for which a segmentation mask is available in each image.A visual hull model is thus used to initialize the iterative deformation process for all these datasets,ex-cept for face and body,where a limited set of viewpoints is available,and the convex hull of the reconstructed patches is used instead.The segmentation mask is also used by our stereo algorithm,which simply ignores the background dur-ingfeature detection and matching.The rim consistency term has only been used in the surface deformation pro-cess for the roman and skull datasets,for which accurate contours are available.The boundingvolume information has not been used tofilter out erroneous matches in our experiments.Our algorithm has successfully reconstructed various surface structures such as the high-curvature and/or shallow surface details of roman,the thin cheek bone and deep eye sockets of skull,and the intricate facial features of face and face-2.Quantitative comparisons kindly pro-vided by D.Scharstein on the datasets presented in[20] show that the proposed method outperforms all the other evaluated techniques in terms of accuracy(distance d such that a given percentage of the reconstruction is within d from the ground truth model)and completeness(percent-age of the ground truth model that is within a given distance from the reconstruction)on four out of the six datasets.The datasets consists of two objects(temple and dino),each of which constitutes three datasets(sparse ring,ring,and full) with different numbers of input images,ranging from16to more than300,and our method achieves the best accuracy and completeness on all the dino datasets and the smallest sparse ring temple.Note that the sparse ring temple and dino datasets consistingof16views have been shown in Fig.7and their quantitative comparison with the top per-formers[4,5,7,18,21,22,23]are g iven in Fig.8.6Fi-nally,the bottom part of Fig.8compares our algorithm with Hern´a ndez Esteban’s method[7],which is one of the best multi-view stereo reconstruction algorithms today,for the polynesian dataset,where a laser scanned model is used as a ground truth.As shown by the close-ups in thisfigure, our model is qualitatively better than the Her´a ndez’s model, especially at sharp concave structures.This is also shown quantitatively usingthe same accuracy and completeness measures as before.Reconstruction results for scene datasets are shown in Fig.9.Additional information(such as segmentation masks,boundingboxes,or valid depth rang es)is not avail-able in this case.The city-hall example is interestingbe-cause viewpoints change significantly across input cameras, and part of the buildingis only visible in some of the frames. Nonetheless,our algorithm has successfully reconstructed the whole scene withfine structural details.The wall dataset is challenging since a large portion of several of the input pictures consists of runningwater,and the corresponding image regions have successfully been detected as outliers, while accurate surface details have been recovered for the rigid wall structure.Finally,Fig.10illustrates our results on crowded scene datasets.Our algorithm reconstructs the background building from the brussels dataset,despite peo-ple occludingvarious parts of the scene.The steps-2dataset is an artificially generated example,where we have manu-ally painted a red cartoonish human in each image of steps images.To further test the robustness of our algorithm against outliers,the steps-3dataset has been created from steps-2by copyingits imag es but replacingthefifth one with the third,without changing camera parameters.This is a particularly challenging example,since the wholefifth image must be detected as an outlier.We have successfully reconstructed the details of both despite these outliers.Note 6Rendered views of the reconstructions and all the quantitative evalua-tions can be found at /mview/.。