RGB - D斯拉姆数据集和基准(RGB-D SLAM Dataset
and Benchmark)
数据介绍：
We provide a large dataset containing RGB-D data and ground-truth data with the goal to establish a novel benchmark for the evaluation of visual odometry and visual SLAM systems. Our dataset contains the color and depth images of a Microsoft Kinect sensor along the
ground-truth trajectory of the sensor. The data was recorded at full frame rate (30 Hz) and sensor resolution (640×480). The ground-truth trajectory was obtained from a high-accuracy motion-capture system with eight high-speed tracking cameras (100 Hz). Further, we provide the accelerometer data from the Kinect. Finally, we propose an evaluation criterion for measuring the quality of the estimated camera trajectory of visual SLAM systems.
关键词：
RGB-D,地面实况,基准,测程,轨迹,
RGB-D,ground-truth,benchmark,odometry,trajectory,
数据格式：
IMAGE
数据详细介绍：
RGB-D SLAM Dataset and Benchmark
Contact: Jürgen Sturm
We provide a large dataset containing RGB-D data and ground-truth data with the goal to establish a novel benchmark for the evaluation of visual odometry and visual SLAM systems. Our dataset contains the color and depth images of a Microsoft Kinect sensor along the ground-truth trajectory of the sensor. The data was recorded at full frame rate (30 Hz) and sensor resolution (640×480). The ground-truth trajectory was obtained from a high-accuracy motion-capture system with eight high-speed tracking cameras (100 Hz). Further, we provide the accelerometer data from the Kinect. Finally, we propose an evaluation criterion for measuring the quality of the estimated camera trajectory of visual SLAM systems.
How can I use the RGB-D Benchmark to evaluate my SLAM system?
1. Download one or more of the RGB-D benchmark sequences (file
formats, useful tools)
2. Run your favorite visual odometry/visual SLAM algorithm (for example,
RGB-D SLAM)
3. Save the estimated camera trajectory to a file (file formats, example
trajectory)
4. Evaluate your algorithm by comparing the estimated trajectory with the
ground truth trajectory. We provide an automated evaluation tool to help you with the evaluation. There is also an online version of the tool. Further remarks
Jose Luis Blanco has added our dataset to the mobile robot programming toolkit (MRPT) repository. The dataset (including example code and tools) can be downloaded here.
?If you have any questions about the dataset/benchmark/evaluation/file formats, please don't hesitate to contact Jürgen Sturm.
?We are happy to share our data with other researchers. Please refer to the respective publication when using this data.
Related publications
2011
Conference and Workshop Papers
Real-Time Visual Odometry from Dense RGB-D Images (F. Steinbruecker, J. Sturm, D. Cremers), In Workshop on Live Dense Reconstruction with Moving Cameras at the Intl. Conf. on Computer Vision (ICCV), 2011. [bib] [pdf] Towards a benchmark for RGB-D SLAM evaluation (J. Sturm, S. Magnenat, N. Engelhard, F. Pomerleau, F. Colas, W. Burgard, D. Cremers, R. Siegwart), In Proc. of the RGB-D Workshop on Advanced Reasoning with Depth Cameras at Robotics: Science and Systems Conf. (RSS), 2011. [bib] [pdf] [pdf]
Real-time 3D visual SLAM with a hand-held camera (N. Engelhard, F. Endres, J. Hess, J. Sturm, W. Burgard), In Proc. of the RGB-D Workshop on 3D Perception in Robotics at the European Robotics Forum, 2011. [bib] [pdf] [video] [video] [video]
File Formats
We provide the RGB-D datasets from the Kinect in the following format:
Color images and depth maps
We provide the time-stamped color and depth images as a gzipped tar file (TGZ).
?The color images are stored as 640×480 8-bit RGB images in PNG format.
?The depth maps are stored as 640×480 16-bit monochrome images in PNG format.
?The color and depth images are already pre-registered using the OpenNI driver from PrimeSense, i.e., the pixels in the color and depth
images correspond already 1:1.
?The depth images are scaled by a factor of 5000, i.e., a pixel value of 5000 in the depth image corresponds to a distance of 1 meter from the
camera, 10000 to 2 meter distance, etc. A pixel value of 0 means
missing value/no data.
Ground-truth trajectories
We provide the groundtruth trajectory as a text file containing the translation and orientation of the camera in a fixed coordinate frame. Note that also our automatic evaluation tool expects both the groundtruth and estimated trajectory to be in this format.
?Each line in the text file contains a single pose.
?The format of each line is 'timestamp tx ty tz qx qy qz qw'
?timestamp (float) gives the number of seconds since the Unix epoch.
?tx ty tz (3 floats) give the position of the optical center of the color camera with respect to the world origin as defined by the motion capture system.
?qx qy qz qw (4 floats) give the orientation of the optical center of the color camera in form of a unit quaternion with respect to the world origin as defined by the motion capture system.
?The file may contain comments that have to start with ”#”.
Intrinsic Camera Calibration of the Kinect
The Kinect has a factory calibration stored onboard, based on a high level polynomial warping function. The OpenNI driver uses this calibration for undistorting the images, and for registering the depth images (taken by the IR camera) to the RGB images. Therefore, the depth images in our datasets are reprojected into the frame of the color camera, which means that there is a 1:1 correspondence between pixels in the depth map and the color image.
The conversion from the 2D images to 3D point clouds works as follows. Note that the focal lengths (fx/fy), the optical center (cx/cy), the distortion parameters (d0-d4) and the depth correction factor are different for each camera. The Python code below illustrates how the 3D point can be computed from the pixel coordinates and the depth value:
fx = 525.0 # focal length x
fy = 525.0 # focal length y
cx = 319.5 # optical center x
cy = 239.5 # optical center y
ds = 1.0 # depth scaling
factor = 5000 # for the 16-bit PNG files
# OR: factor = 1 # for the 32-bit float images in the ROS bag files
for v in range(depth_image.height):
for u in range(depth_image.width):
Z = (depth_image[v,u] / factor) * ds;
X = (u - cx) * Z / fx;
Y = (v - cy) * Z / fy;
Note that the above script uses the default (uncalibrated) intrinsic parameters. The intrinsic parameters for the Kinects used in the fr1 and fr2 dataset are as follows:
Calibration of the color camera
We computed the intrinsic parameters of the RGB camera from the
rgbd_dataset_freiburg1/2_rgb_calibration.bag.
Camera fx fy cx cy d0 d1 d2 d3 d4
(ROS
525.0 525.0 319.5 239.5 0.0 0.0 0.0 0.0 0.0 default)
Freiburg 1
517.3 516.5 318.6 255.3 0.2624 -0.9531 -0.0054 0.0026 1.1633 RGB
Freiburg 2
520.9 521.0 325.1 249.7 0.2312 -0.7849 -0.0033 -0.0001 0.9172 RGB
Calibration of the depth images
We verified the depth values by comparing the reported depth values to the depth estimated from the RGB checkerboard. In this experiment, we found that the reported depth values from the Kinect were off by a constant scaling factor, as given in the following table:
Camera ds
Freiburg 1 Depth 1.035
Freiburg 2 Depth 1.031
Calibration of the infrared camera
We also provide the intrinsic parameters for the infrared
camera. Note that the depth images provided in our dataset are already
pre-registered to the RGB images. Therefore, rectifying the depth images based on the intrinsic parameters is not straight forward.
Camera fx fy cx cy d0 d1 d2 d3 d4 Freiburg 1 IR591.1 590.1 331.0 234.0 -0.0410 0.3286 0.0087 0.0051 -0.5643
Freiburg 2 IR580.8 581.8 308.8 253.0 -0.2297 1.4766 0.0005 -0.0075 -3.4194 Movies for visual inspection
For visual inspection of the individual datasets, we also provide movies of the Kinect (RGB and depth) and of an external camcorder. The movie format is mpeg4 stored in an AVI container.
Alternate file formats
ROS bag
For people using ROS, we also provide ROS bag files that contain the color images, monochrome images, depth images, camera infos, point clouds and transforms – including the groundtruth transformation from the /world frame all in a single file. The bag files (ROS diamondback) contain the following message topics:
?/camera/depth/camera_info (sensor_msgs/CameraInfo) contains the intrinsic camera parameters for the depth/infrared camera, as
reported by the OpenNI driver
?/camera/depth/image (sensor_msgs/Image) contains the depth map ?/camera/rgb/camera_info (sensor_msgs/CameraInfo) contains the intrinsic camera parameters for the RGB camera, as reported by the
OpenNI driver
?/camera/rgb/image_color (sensor_msgs/Image) contains the color image from the RGB camera
?/imu (sensor_msgs/Imu), contains the accelerometer data from the Kinect
?/tf (tf/tfMessage), contains:
o the ground-truth data from the mocap (/world to /Kinect)
o the calibration betwenn mocap and the optical center of the Kinect's color camera (/Kinect to /openni_camera),
o and the ROS-specific, internal transformations (/openni_camera to /openni_rgb_frame to /openni_rgb_optical_frame).
If you need the point clouds and monochrome images, you can use the adding_point_clouds_to_ros_bag_files script to add them:
?/camera/rgb/image_mono (sensor_msgs/Image) contains the monochrome image from the RGB camera
?/camera/rgb/points (sensor_msgs/PointCloud2) contains the colored point clouds
/camera/depth/points (sensor_msgs/PointCloud2) contains the point cloud
Mobile Robot Programming Toolkit (MRPT)
Jose Luis Blanco has added our dataset to the mobile robot programming toolkit (MRPT) repository. The dataset (including example code and tools) can be downloaded here
Useful tools for the RGB-D benchmark
We provide a set of tools that can be used to pre-process the datasets and to evaluate the SLAM/tracking results. The scripts can be downloaded here.
To checkout the repository using subversion, run
svn checkout
https://svncvpr.in.tum.de/cvpr-ros-pkg/trunk/rgbd_benchmark/rgbd_benchmar k_tools
Associating color and depth images
The Kinect provides the color and depth images in an un-synchronized way. This means that the set of time stamps from the color images do not intersect with those of the depth images. Therefore, we need some way of associating color images to depth images.
For this purpose, you can use the ''associate.py'' script. It reads the time stamps from the rgb.txt file and the depth.txt file, and joins them by finding the best matches.
usage: associate.py [-h] [--first_only] [--offset OFFSET]
[--max_difference MAX_DIFFERENCE]
first_file second_file
This script takes two data files with timestamps and associates them positional arguments:
first_file first text file (format: timestamp data)
second_file second text file (format: timestamp data)
optional arguments:
-h, --help show this help message and exit
--first_only only output associated lines from first file
--offset OFFSET time offset added to the timestamps of the second file
(default: 0.0)
--max_difference MAX_DIFFERENCE
maximally allowed time difference for matching entries
(default: 0.02)
Evaluation
After estimating the camera trajectory of the Kinect and saving it to a file, we need to evaluate the error in the estimated trajectory by comparing it with the ground-truth. There are different error metrics. Two prominent methods is the absolute trajectory error (ATE) and the relative pose error (RPE). The ATE is well-suited for measuring the performance of visual SLAM systems. In contrast, the RPE is well-suited for measuring the drift of a visual odometry system, for example the drift per second.
For both metrics, we provide automated evaluation scripts that can be downloaded here. Note that there is also an online version available on our website. Both trajectories have to be stored in a text file (format: 'timestamp tx ty tz qx qy qz qw', more information). For comparison, we offer a set of trajectories from RGBD-SLAM.
Absolute Trajectory Error (ATE)
The absolute trajectory error directly measures the difference between points of the true and the estimated trajectory. As a pre-processing step, we associate the estimated poses with ground truth poses using the timestamps. Based on this association, we align the true and the estimated trajectory using singular value decomposition. Finally, we compute the difference between each pair of poses, and output the mean/median/standard deviation of these differences. Optionally, the script can plot both trajectories to a png or pdf file. usage: evaluate_ate.py [-h] [--offset OFFSET] [--scale SCALE]
[--max_difference MAX_DIFFERENCE] [--save SAVE]
[--save_associations SAVE_ASSOCIATIONS]
[--plot PLOT]
[--verbose]
first_file second_file
This script computes the absolute trajectory error from the ground truth
trajectory and the estimated trajectory.
positional arguments:
first_file first text file (format: timestamp tx ty tz qx qy qz
qw)
second_file second text file (format: timestamp tx ty tz qx qy qz
qw)
optional arguments:
-h, --help show this help message and exit
--offset OFFSET time offset added to the timestamps of the second file
(default: 0.0)
--scale SCALE scaling factor for the second trajectory (default:
1.0)
--max_difference MAX_DIFFERENCE
maximally allowed time difference for matching entries
(default: 0.02)
--save SAVE save aligned second trajectory to disk (format: stamp2
x2 y2 z2)
--save_associations SAVE_ASSOCIATIONS
save associated first and aligned second trajectory to
disk (format: stamp1 x1 y1 z1 stamp2 x2 y2 z2)
--plot PLOT plot the first and the aligned second trajectory to an
image (format: png)
--verbose print all evaluation data (otherwise, only the RMSE
absolute translational error in meters after alignment
will be printed)
Relative Pose Error (RPE)
For computing the relative pose error, we provide a script ''evaluate_rpe.py''. This script computes the error in the relative motion between pairs of timestamps. By default, the script computes the error between all pairs of timestamps in the estimated trajectory file. As the number of timestamp pairs in the estimated trajectory is quadratic in the length of the trajectory, it can make sense to downsample this set to a fixed number (–max_pairs). Alternatively, one can choose to use a fixed window size (–fixed_delta). In this case, each pose in the estimated trajectory is associated with a later pose according to the window size (–delta) and unit (–delta_unit). This evaluation technique is useful for estimating the drift.
usage: evaluate_rpe.py [-h] [--max_pairs MAX_PAIRS] [--fixed_delta]
[--delta DELTA] [--delta_unit DELTA_UNIT]
[--offset OFFSET] [--scale SCALE] [--save SAVE]
[--plot PLOT] [--verbose]
groundtruth_file estimated_file
This script computes the relative pose error from the ground truth trajectory and the estimated trajectory.
positional arguments:
groundtruth_file ground-truth trajectory file (format: "timestamp tx ty
tz qx qy qz qw")
estimated_file estimated trajectory file (format: "timestamp tx ty tz
qx qy qz qw")
optional arguments:
-h, --help show this help message and exit
--max_pairs MAX_PAIRS
maximum number of pose comparisons (default: 10000,
set to zero to disable downsampling)
--fixed_delta only consider pose pairs that have a distance of delta
delta_unit (e.g., for evaluating the drift per
second/meter/radian)
--delta DELTA delta for evaluation (default: 1.0)
--delta_unit DELTA_UNIT
unit of delta (options: 's' for seconds, 'm' for
meters, 'rad' for radians, 'f' for frames; default:
's')
--offset OFFSET time offset between ground-truth and estimated
trajectory (default: 0.0)
--scale SCALE scaling factor for the estimated trajectory (default:
1.0)
--save SAVE text file to which the evaluation will be saved
(format: stamp_est0 stamp_est1 stamp_gt0
stamp_gt1
trans_error rot_error)
--plot PLOT plot the result to a file (requires --fixed_delta,
output format: png)
--verbose print all evaluation data (otherwise, only the mean
translational error measured in meters will be
printed)
Generating a point cloud from images
The depth images are already registered to the color images, so the pixels in the depth image already correspond one-to-one to the pixels in the color image. Therefore, generating colored point clouds is straight-forward. An example script is available in ''generate_pointcloud.py'', that takes a color image and a depth map as input, and generates a point cloud file in the PLY format. This format can be read by many 3D modelling programs, for example meshlab. You can download meshlab for Windows, Mac and Linux.
usage: generate_pointcloud.py [-h] rgb_file depth_file ply_file
This script reads a registered pair of color and depth images and generates a colored 3D point cloud in the PLY format.
positional arguments:
rgb_file input color image (format:
png)
depth_file input depth image (format: png)
ply_file output PLY file (format: ply)
optional arguments:
-h, --help show this help message and exit
Adding point clouds to ROS bag files
On the download page, we already provide ROS bag files with added point clouds for the datasets for visual inspection in RVIZ. Because of the large size of the resulting files, we downsampled these bag files to 2 Hz. In case that you want to generate ROS bag files that contain the point clouds for all images (at 30 Hz), you can use the ''add_pointclouds_to_bagfile.py'' script.
usage: add_pointclouds_to_bagfile.py [-h] [--start START]
[--duration DURATION] [--nth NTH]
[--skip SKIP] [--compress]
inputbag [outputbag]
This scripts reads a bag file containing RGBD data, adds the corresponding PointCloud2 messages, and saves it again into a bag file. Optional arguments
allow to select only a portion of the original bag file.
positional arguments:
inputbag input bag file
outputbag output bag file
optional arguments:
-h, --help show this help message and exit
--start START skip the first N seconds of input bag file (default:
0.0)
--duration DURATION only process N seconds of input bag file (default: off)
--nth NTH only process every N-th frame of input bag file
(default: 15)
--skip SKIP skip N blocks in the beginning (default: 1)
--compress compress output bag file
Visualizing the datasets in RVIZ
RVIZ is the standard visualization tool in ROS. It can be easily adapted to display many different messages. In particular, it can be used to display the point clouds from a ROS bag file. For this, run (in three different consoles) roscore
rosrun rviz rviz
rosbag play rgbd_dataset_freiburg1_xyz-2hz-with-pointclouds.bag
If this is the first launch, you will have to enable the built-in displays (Menu –> Plugins –> Check “Loaded” for the builtin plugins). In the displays tab, set the “fixed frame” to ”/world”. Click on “Add”, and select the PointCloud2 display, and set topic to ”/camera/rgb/points”. To show the colors, change “color transformer” to “RGB8” in the point cloud display and the “style”to “points”. If you want, you can set the decay time to a suitable value, for example 5 seconds, to accumulate the points in the viewer as they come in. The result should then look as follows:
数据预览：
点此下载完整数据集
常见颜色的RGB值 2007年11月04日星期日 21:01 128/0/0 深红 255/0/0 红 255/0/255 粉红 255/153/204 玫瑰红 153/51/0 褐色 255/102/0 桔黄 255/153/0 浅桔黄 255/204/0 金色 255/204/153 棕黄 51/51/0 橄榄绿 128/128/0 深黄 153/204/0 酸橙色 255/255/0 黄色 255/255/153 浅黄 0/51/0 深绿 0/128/0 绿色 51/153/102 海绿 0/255/0 鲜绿 204/255/204 浅绿 0/51/102 深灰蓝 0/128/128 青色 51/204/204 宝石蓝 0/255/255 青绿 204/255/255 浅青绿 0/0/128 深蓝 0/0/255 蓝色 51/102/255 浅蓝 0/204/255 天蓝 153/204/255 浅蓝 51/51/153 靛蓝 102/102/153 蓝灰 128/0/128 紫色 153/51/102 梅红 204/153/255 淡紫 51/51/51 80%灰 128/128/128 50%灰 153/153/153 40%灰 192/192/192 25%灰 常见颜色的RGB值
(2008-05-10 14:51:24) 分类：经验交流标签：颜色rgb红色黄色紫色银色蓝色校 园 颜色 R G B 白色：FFFFFF 红色：FF0000 绿色：00FF00 蓝色：0000FF 洋红：FF00FF 墨绿：00FFFF 黄色：FFFF00 黑色：000000 爱丽丝兰：F0F8FF 碧绿：70DB93 巧克力色：5C3317 蓝紫色：9F5F9F 黄铜：B5A642 亮金：D9D919 褐色：A62AA2 青铜：8C7853 青铜2：A67D3D 藏青：5F9F9F 亮铜：D98719 铜色：B87333 珊瑚色：FF7F00 矢车菊兰：42426F 深褐色：5C4033 深绿色：2F4F2F 深铜绿色：4A766E 深橄榄绿：4F4F2F 紫色：9932CD 深紫色：871F78 深石板蓝：6B238E 深石板灰：2F4F4F 深黄褐色：97694F 深蓝玉色：7093DB 暗木色：855E42 暗灰：545454 暗玫瑰色：856363 长石色：D19275 砖红色：8E2323
国际色彩标准名称与色值
Magenta洋红 (品红玫瑰红) Fuchsia灯笼海棠(紫红色) DarkMagenta深洋红 Purple紫色MediumOrchid中兰花紫 DarkViolet暗紫罗兰 DarkOrchid暗兰花紫 Indigo靛青 (紫兰色) BlueViolet蓝紫罗兰MediumPurple中紫色MediumSlateBlue中板岩蓝SlateBlue板岩蓝DarkSlateBlue暗板岩蓝Lavender熏衣草淡紫 GhostWhite幽灵白
Blue纯蓝MediumBlue中蓝色MidnightBlue午夜蓝 DarkBlue暗蓝色 Navy海军蓝RoyalBlue皇家蓝 (宝蓝) CornflowerBlue矢车菊蓝LightSteelBlue亮钢蓝LightSlateGray亮石板灰SlateGray石板灰DodgerBlue道奇蓝 AliceBlue爱丽丝蓝 SteelBlue钢蓝 (铁青) LightSkyBlue亮天蓝色SkyBlue天蓝色
DeepSkyBlue深天蓝 LightBlue亮蓝 PowderBlue火药青 CadetBlue军服蓝 Azure蔚蓝色 LightCyan淡青色PaleTurquoise弱绿宝石Cyan青色 Aqua水色DarkTurquoise暗绿宝石DarkSlateGray暗石板灰DarkCyan暗青色 Teal水鸭色MediumTurquoise中绿宝石LightSeaGreen 浅海洋绿
Turquoise绿宝石 Aquamarine宝石碧绿MediumAquamarine中宝石碧绿MediumSpringGreen中春绿色MintCream薄荷奶油 SpringGreen春绿色MediumSeaGreen中海洋绿SeaGreen海洋绿 Honeydew蜜瓜色 LightGreen淡绿色 PaleGreen弱绿色 DarkSeaGreen暗海洋绿 LimeGreen闪光深绿Lime闪光绿ForestGreen森林绿
数字"1"开头的 1000 Green beige 米绿色 1001 Beige 米色，淡黄或灰黄1002 Sand yellow 沙黄色 1003 Signal yellow 信号黄 1004 Goldenyellow 金黄色 1005 Honey yellow 蜜黄色 1006 Maize yellow 玉米黄 1007 Daffodil yellow 灰黄色 1011 Brown beige 米褐色 1012 Lemon yellow 柠檬黄 1013 Oyster white 近于白色的浅灰1014 Ivory 象牙色 1015 Light ivory 亮象牙色 1016 Sulfur yellow 硫磺色 1017 Saffron yellow 深黄色 1018 Zinc yellow 绿黄色 1019 Grey beige 米灰色 1020 Olive yellow 橄榄黄 1021 Rape yellow 油菜黄 1023 Traffic yellow 交通黄 1024 Ochre yellow 赭黄色 1026 Luminous yellow 亮黄色1027 Curry 咖喱色 1028 Melon yellow 浅橙黄 1032 Broom yellow 金雀花黄1033 Dahlia yellow 大丽花黄1034 Pastel yellow 粉黄色 1035 Pearl beige 米珍珠色 1036 Pearl gold 金黄珍珠 1037 Sun yellow 日光黄 数字“2”开头的 2000 Yellow orange 黄橙色 2001 Red orange 橘红 2002 Vermilion 朱红 2003 Paster orange 淡橙 2004 Pure orange 纯橙 2005 Luminous orange 亮橙 2007 Luminous bright orange 亮浅橙 2008 Bright red orange 浅红橙2009 Traffic orange 交通橙 2011 Deep orange 深橙色 2012 Salmon orange 鲑鱼橙 2013 Pearl orange 珍珠橙 数字“3”开头的 3000 Flame red 火焰红 3001 Signal red 信号红 3002 Carmine red 胭脂红 3003 Ruby red 宝石红 3004 Purple red 紫红色 3012 Beige red 米红色 3013 Tomato red 番茄红 3014 Antique pink 古粉红色 3015 Light pink 淡粉红色 3016 Coral red 珊瑚红色 3017 Rose 玫瑰色3018 Strawberry red 草莓红 3020 Traffic red 交通红 3022 Salmon pink 鲑鱼粉红色 3024 Luminous red 亮红色 3026 Luminous bright red 淡亮红 色 3027 Raspbery red 悬钩子红色 3031 Orient red 戈亚红色 3005 Wine red 葡萄酒红 3007 Black red 黑红色 3009 Oxide red 氧化红 3011 Brown red 红玄武土色 3032 Pearl ruby red 红宝石珍珠红 3033 Pearl pink 珍珠红色 数字“4”开头的 4001 Red lilac 丁香红 4002 Red violet 紫红色 4003 Heather violet 石南紫 4004 Claret violet 酒红紫 4005 Blue lilac 丁香蓝 4006 Traffic purple 交通紫 4007 Purple violet 紫红蓝色 4008 Signal violet 信号紫罗兰 4009 Pastel violet 崧蓝紫色 4010 Telemagenta 电视品红色 4011 Pearl violet 珍珠紫 4012 Peal blackberry 珍珠黑 数字“5”开头的 5000 Violet blue 紫蓝色 5001 Green blue 蓝绿色 5002 Ultramarine blue 群青蓝 5003 Sapphire blue 蓝宝石蓝 5004 Black blue 蓝黑色 5005 Signal blue 信号蓝 5007 Brillant blue 亮蓝色 5008 Grey blue 灰蓝色 5009 Azure blue 天青蓝 5010 Gentian blue 龙胆蓝色 5011 Steel blue 钢蓝色 5012 Light blue 淡蓝色 5013 Cobalt blue 钴蓝色 5014 Pigeon blue 鸽蓝色 5015 Sky blue 天蓝色 5017 Traffic blue 交通蓝 5018 Turquoise blue 绿松石蓝 5019 Capri blue 卡布里蓝色 5020 Ocean blue 海蓝色 5021 Water blue 不来梅蓝色 5022 Night blue 夜蓝色 5023 Distant blue 冷蓝色 5024 Pastel blue 崧蓝蓝色 5025 Pearl gentian blue 珍珠龙胆 蓝 5026 Pearl night blue 珍珠夜蓝 数字“6”开头的 6002 Leaf green 叶绿色 6003 Olive green 橄榄绿 6004 Blue green 蓝绿色 6005 Moss green 苔藓绿 6006 Grey olrve 橄榄灰绿 6000 Patina green 铜锈绿色 6001 Emerald greet 翡翠绿色 6032 Signal greet 信号绿 6033 Mint turquoise 薄荷绿蓝色 6034 Pastel turquoies 崧蓝绿松石 色 6035 Pearl green 珍珠绿 6036 Pearl opal green 不透明蓝白 绿 6007 Bottle green 瓶绿 6008 Brown green 褐绿 6009 Fir greet 冷杉绿 6010 Grass greet 草绿色 6011 Reseda green 淡橄榄绿 6012 Black green 墨绿色 6013 Reed green 芦苇绿 6014 Yellow olive 橄榄黄 6015 Black olive 黑齐墩果色 6016 Turquoise green 绿松石绿色 6017 May green 五月红 6018 Yellow green 黄绿色 6019 Pastel green 崧蓝绿色 6020 Chrome green 铭绿色 6021 Pale green 浅绿色 6022 Olive drab 橄榄土褐色 6024 Traffic green 交通绿 6025 Fern green 蕨绿色 6026 Opal green 蛋白石绿色 6027 Light green 浅绿色 6028 Pine green 松绿色 6029 Mint green 薄荷绿 数字“7”开头的 7000 Squirrel grey 松鼠灰 7001 Silver grey 银灰色 7002 Olive grey 橄榄灰绿色 7003 Moss grey 苔藓绿 7004 Signal grey 信号灰 7005 Mouse grey 鼠灰色 7006 Beige grey 米灰色 7008 Khaki grey 土黄灰色 7009 Green grey 绿灰色 7010 Tarpaulin gey 油布灰 7011 Iron grey 铁灰色 7012 Basalt grey 玄武石灰 7013 Brown grey 褐灰色 7015 Slate grey 浅橄榄灰 7016 Anthracite grey 煤灰 7021 Black grey 黑灰 7022 Umbra grey 暗灰 7023 Concrete grey 混凝土灰 7032 Pebble grey 卵石灰 7033 Cement grey 水泥灰 7034 Yellow grey 黄灰色 7035 Light grey 浅灰色 7036 Platinum grey 铂灰色 7037 Dusty grey 土灰色 7038 Agate grey 玛瑙灰 7039 Quartz grey 石英灰 7040 Window grey 窗灰色 7042 Traffic grey A 交通灰A 7043 Traffic grey B 交通灰B 7044 Silk grey 深铭灰色 7045 Telegrey 1 电视灰1 7046 Telegrey 2 电视灰2 7047 Telegrey 4 电视灰4 7048 Pearl mouse grey 珍珠鼠灰 7024 Graphite grey 石墨灰 7026 Granite grey 花岗灰 7030 Stone grey 石灰色 7031 Blue grey 蓝灰色 数字“8”开头的 8000 Green brown 绿褐色 8001 Ochre brown 赭石棕色 8002 Signal brown 信号褐 8003 Clay brown 土棕褐色 8004 Coper brown 铜棕色 8007 Fawn brown 鹿褐色 8008 Olive brown 橄榄棕色 8011 Nut brown 深棕色 8012 Red brown 红褐色 8014 Sepia brown 乌贼棕色 8015 Chestnut brown 粟棕色 8016 Mahogany brown 桃花心木褐 8017 Chocolate brown 巧克力棕色 8019 Grey brown 灰褐色 8022 Black brown 黑褐色 8023 Orange brown 桔黄褐 8024 Beige brown 哔叽棕色 8025 Pale brown 浅褐色 8028 Terra brown 浅灰褐色 8029 Pearl copper 珍珠铜棕色 数字“9”开头的 9001 Cream 彩黄色 9002 Grey white 灰白色 9003 Signal white 信号白 9004 Signal black 信号黑 9005 Jet black 墨黑色 9006 White aluminium 白铝灰色 9007 Grey aluminium 灰铝色 9010 Pure white 纯白色 9011 Graphiack 石墨黑 9016 Traffic white 交通白 9017 Traffic black 交通黑 9018 Papyrus white 草纸白 9022 Pearl light grey 珍珠浅灰 9023 Pearl dark grey 珍珠深灰 RAL工业国际标准色卡对照表 2009-06-06 21:54 RAL工业国际标准色卡对照表
国际色彩标准名称与色 值 文稿归稿存档编号：[KKUY-KKIO69-OTM243-OLUI129-G00I-FDQS58-
国际色彩标准名称与色值
Blue 纯蓝 MediumBlue 中蓝色 MidnightBlue 午夜蓝 DarkBlue 暗蓝色 Navy 海军蓝 RoyalBlue 皇家蓝 (宝 蓝) CornflowerBlue 矢车菊蓝 LightSteelBlue 亮钢蓝 LightSlateGray 亮石板灰 SlateGray 石板灰 DodgerBlue 道奇蓝 AliceBlue 爱丽丝蓝 SteelBlue 钢蓝 (铁 青) LightSkyBlue 亮天蓝色 SkyBlue 天蓝色 DeepSkyBlue 深天蓝 LightBlue 亮蓝 PowderBlue 火药青 CadetBlue 军服蓝 Azure 蔚蓝色 LightCyan 淡青色 PaleTurquoise 弱绿宝石 Cyan 青色 Aqua 水色 DarkTurquoise 暗绿宝石 DarkSlateGray 暗石板灰 DarkCyan 暗青色 Teal 水鸭色 MediumTurquoise 中绿宝石 LightSeaGreen 浅海洋绿 Turquoise 绿宝石 Aquamarine 宝石碧绿
MediumAquamarine 中宝石碧绿 MediumSpringGreen 中春绿色 MintCream 薄荷奶油 SpringGreen 春绿色 MediumSeaGreen 中海洋绿 SeaGreen 海洋绿 Honeydew 蜜瓜色 LightGreen 淡绿色 PaleGreen 弱绿色 DarkSeaGreen 暗海洋绿 LimeGreen 闪光深绿 Lime 闪光绿 ForestGreen 森林绿 Green 纯绿 DarkGreen 暗绿色 Chartreuse** 查特酒绿 (黄绿色) LawnGreen 草坪绿 GreenYellow 绿黄色 DarkOliveGreen 暗橄榄绿 YellowGreen 黄绿色 OliveDrab 橄榄褐色 Beige 米色(灰棕 色) 亮菊黄 Ivory 象牙 LightYellow 浅黄色 Yellow 纯黄 Olive 橄榄 DarkKhaki 深卡叽布 LemonChiffon 柠檬绸 PaleGoldenrod 灰菊黄 Khaki 卡叽布 Gold 金色
如对你有帮助，请购买下载打赏，谢谢！ 国际色彩标准名称与色值 LightPink 浅粉红#FFB6C1 255,182,193 Pink 粉红#FFC0CB 255,192,203 Crimson 猩红(深红) #DC143C 220,20,60 LavenderBlush 淡紫红#FFF0F5 255,240,245 PaleVioletRed 弱紫罗兰红#DB7093 219,112,147 HotPink 热情的粉红#FF69B4 255,105,180 DeepPink 深粉红#FF1493 255,20,147 MediumVioletRed 中紫罗兰红#C71585 199,21,133 Orchid 兰花紫#DA70D6 218,112,214 Thistle 蓟#D8BFD8 216,191,216 Plum 李子紫#DDA0DD 221,160,221 Violet 紫罗兰#EE82EE 238,130,238 Magenta 洋红(品红玫瑰红) #FF00FF 255,0,255 Fuchsia 灯笼海棠(紫红色) #FF00FF 255,0,255 DarkMagenta 深洋红#8B008B 139,0,139 Purple 紫色#800080 128,0,128 MediumOrchid 中兰花紫#BA55D3 186,85,211 DarkViolet 暗紫罗兰#9400D3 148,0,211 DarkOrchid 暗兰花紫#9932CC 153,50,204 Indigo 靛青(紫兰色) #4B0082 75,0,130 BlueViolet 蓝紫罗兰#8A2BE2 138,43,226 MediumPurple 中紫色#9370DB 147,112,219 MediumSlateBlue 中板岩蓝#7B68EE 123,104,238 SlateBlue 板岩蓝#6A5ACD 106,90,205 DarkSlateBlue 暗板岩蓝#483D8B 72,61,139 Lavender 熏衣草淡紫#E6E6FA 230,230,250 GhostWhite 幽灵白#F8F8FF 248,248,255 Blue 纯蓝#0000FF 0,0,255 MediumBlue 中蓝色#0000CD 0,0,205 MidnightBlue 午夜蓝#191970 25,25,112 DarkBlue 暗蓝色#00008B 0,0,139 Navy 海军蓝#000080 0,0,128 RoyalBlue 皇家蓝(宝蓝) #4169E1 65,105,225
数字?开头的 3013 Tomato red 番茄红 5021 Water blue 不来梅蓝色 7008 Khaki grey 土黄灰色 7009 Green grey 米绿色绿灰色3014 Antique pink 古粉红色 5022 Night blue 夜蓝色 1000 Green beige 7010 Tarpaulin gey 1001 Beige 米色，淡黄或灰黄油布灰3015 Light pink 淡粉红色 5023 Distant blue 冷蓝色7011 Iron grey 5024 Pastel blue 1002 Sand yellow 沙黄色崧蓝蓝色 3016 Coral red 珊瑚红色铁灰色 7012 Basalt grey 玄武石灰珍珠龙胆1003 Signal yellow 信号黄 5025 Pearl gentian blue 3017 Rose 玫瑰色 7013 Brown grey 褐灰色 1004 Goldenyellow 金黄色蓝 3018 Strawberry red 草莓红7015 Slate grey 交通红浅橄榄灰 1005 Honey yellow 蜜黄色 5026 Pearl night blue 珍珠夜蓝3020 Traffic red 7016 Anthracite grey 数字“6”开头的3022 Salmon pink 鲑鱼粉红色1006 Maize yellow 玉米黄煤灰 7021 Black grey 黑灰3024 Luminous red 亮红色 1007 Daffodil yellow 灰黄色 6002 Leaf green 叶绿色 7022 Umbra grey 暗灰 3026 Luminous bright red 1011 Brown beige 米褐色淡亮红 6003 Olive green 橄榄绿7023 Concrete grey 6004 Blue green 混凝土灰色 1012 Lemon yellow 柠檬黄蓝绿色 7032 Pebble grey 卵石灰6005 Moss green 悬钩子红色3027 Raspbery red 1013 Oyster white 近于白色的浅灰苔藓绿 7033 Cement grey 6006 Grey olrve 3031 Orient red 戈亚红色1014 Ivory 象牙色橄榄灰绿水泥灰7034 Yellow grey 6000 Patina green 铜锈绿色亮象牙色1015 Light ivory 3005 Wine red 葡萄酒红黄灰色 7035 Light grey 浅灰色 1016 Sulfur yellow 硫磺色3007 Black red 黑红色 6001 Emerald greet 翡翠绿色 7036 Platinum grey 1017 Saffron yellow 深黄色氧化红3009 Oxide red 铂灰色 6032 Signal greet 信号绿 6033 Mint turquoise 1018 Zinc yellow 绿黄色 3011 Brown red 红玄武土色薄荷绿蓝色7037 Dusty grey 土灰色玛瑙灰 3032 Pearl ruby red 红宝石珍珠红7038 Agate grey 崧蓝绿松石6034 Pastel turquoies 米灰色1019 Grey beige 3033 Pearl pink 石英灰珍珠红色 7039 Quartz grey 1020 Olive yellow 橄榄黄色数字“4”开头 的 6035 Pearl green 珍珠绿窗灰色1021 Rape yellow 油菜黄 7040 Window grey A 4001 Red lilac 交通灰6036 Pearl opal green 丁香红 1023 Traffic yellow 交通黄不透明蓝白7042 Traffic grey A B 4002 Red violet 紫红色交通灰绿赭黄色1024 Ochre yellow 7043 Traffic grey B 石南紫4003 Heather violet 6007 Bottle green 1026 Luminous yellow 亮黄色瓶绿7044 Silk grey 深铭灰色1 4004 Claret violet 酒红紫褐绿咖喱色1027 Curry 7045 Telegrey 1 电视灰 6008 Brown green 2 7046 Telegrey 2 6009 Fir greet 冷杉绿电视灰4005 Blue lilac 丁香蓝 1028 Melon yellow 浅 橙黄4 1032 Broom yellow 金雀花黄草绿色 7047 Telegrey 4 4006 Traffic purple 交通紫电视灰 6010 Grass greet 淡橄榄绿6011 Reseda green 紫红蓝色4007 Purple violet 大丽花黄1033 Dahlia yellow 7048 Pearl mouse grey 珍珠鼠灰墨绿色 4008 Signal violet 信号紫罗兰 1034 Pastel yellow 粉黄色7024 Graphite grey 石墨灰6012 Black green 崧蓝紫色4009 Pastel violet 米珍珠色1035 Pearl beige 6013 Reed green 芦苇绿 7026 Granite grey 花岗灰金黄珍珠7030 Stone grey 石灰色 6014 Yellow olive 橄榄黄4010 Telemagenta 电视品红色1036 Pearl gold 1037 Sun yellow 蓝灰色黑齐墩果色6015 Black olive 珍珠紫4011 Pearl violet 日光黄 7031 Blue
国际色彩标准名称与色值 2007-08-11 Named Numeric Color Name Hex RGB Decimal LightPink 浅粉红#FFB6C1 255,182,193 Pink 粉红#FFC0CB 255,192,203 Crimson 猩红(深 红) #DC143C 220,20,60 LavenderBlush 淡紫红#FFF0F5 255,240,245 PaleVioletRed 弱紫罗兰 红 #DB7093 219,112,147 HotPink 热情的粉 红 #FF69B4 255,105,180 DeepPink 深粉红#FF1493 255,20,147 MediumVioletRed 中紫罗兰 红 #C71585 199,21,133 Orchid 兰花紫#DA70D6 218,112,214 Thistle 蓟#D8BFD8 216,191,216 Plum 李子紫#DDA0DD 221,160,221 Violet 紫罗兰#EE82EE 238,130,238
Magenta 洋红(品 红玫瑰 红) #FF00FF 255,0,255 Fuchsia 灯笼海棠 (紫红色) #FF00FF 255,0,255 DarkMagenta 深洋红#8B008B 139,0,139 Purple 紫色#800080 128,0,128 MediumOrchid 中兰花紫#BA55D3 186,85,211 DarkViolet 暗紫罗兰#9400D3 148,0,211 DarkOrchid 暗兰花紫#9932CC 153,50,204 Indigo 靛青(紫 兰色) #4B0082 75,0,130 BlueViolet 蓝紫罗兰#8A2BE2 138,43,226 MediumPurple 中紫色#9370DB 147,112,219 MediumSlateBlue 中板岩蓝#7B68EE 123,104,238 SlateBlue 板岩蓝#6A5ACD 106,90,205 DarkSlateBlue 暗板岩蓝#483D8B 72,61,139 Lavender 熏衣草淡 紫 #E6E6FA 230,230,250 GhostWhite 幽灵白#F8F8FF 248,248,255
工业国际标准色卡对照表1000 Green beige 米绿色 1001 Beige 米色，淡黄或灰黄 1002 Sand yellow 沙黄色 1003 Signal yellow 信号黄 1004 Goldenyellow 金黄色 1005 Honey yellow 蜜黄色 1006 Maize yellow 玉米黄 1007 Daffodil yellow 灰黄色 1011 Brown beige 米褐色 1012 Lemon yellow 柠檬黄 1013 Oyster white 近于白色的浅灰 1014 Ivory 象牙色 1015 Light ivory 亮象牙色 1016 Sulfur yellow 硫磺色 1017 Saffron yellow 深黄色 1018 Zinc yellow 绿黄色 1019 Grey beige 米灰色 1020 Olive yellow 橄榄黄 1021 Rape yellow 油菜黄 1023 Traffic yellow 交通黄 1024 Ochre yellow 赭黄色
1026 Luminous yellow 亮黄色 1027 Curry 咖喱色 1028 Melon yellow 浅橙黄 1032 Broom yellow 金雀花黄 1033 Dahlia yellow 大丽花黄 1034 Pastel yellow 粉黄色 1035 Pearl beige 米珍珠色 1036 Pearl gold 金黄珍珠 1037 Sun yellow 日光黄 2000 Yellow orange 黄橙色 2001 Red orange 橘红 2002 Vermilion 朱红 2003 Paster orange 淡橙 2004 Pure orange 纯橙 2005 Luminous orange 亮橙 2007 Luminous bright orange 亮浅橙2008 Bright red orange 浅红橙2009 Traffic orange 交通橙 2011 Deep orange 深橙色 2012 Salmon orange 鲑鱼橙 2013 Pearl orange 珍珠橙
国际色彩标准名称与色值 Named Numeric Color Name Hex RGB Decimal LightPink 浅粉红#FFB6C1 255,182,193 Pink 粉红#FFC0CB 255,192,203 Crimson 猩红(深红) #DC143C 220,20,60 LavenderBlush 淡紫红#FFF0F5 255,240,245 PaleVioletRed 弱紫罗兰红#DB7093 219,112,147 HotPink 热情的粉红#FF69B4 255,105,180 DeepPink 深粉红#FF1493 255,20,147 MediumVioletRed 中紫罗兰红#C71585 199,21,133 Orchid 兰花紫#DA70D6 218,112,214 Thistle 蓟#D8BFD8 216,191,216 Plum 李子紫#DDA0DD 221,160,221 Violet 紫罗兰#EE82EE 238,130,238 Magenta 洋红(品红玫瑰红) #FF00FF 255,0,255 Fuchsia 灯笼海棠(紫红色) #FF00FF 255,0,255 DarkMagenta 深洋红#8B008B 139,0,139 Purple 紫色#800080 128,0,128 MediumOrchid 中兰花紫#BA55D3 186,85,211 DarkViolet 暗紫罗兰#9400D3 148,0,211 DarkOrchid 暗兰花紫#9932CC 153,50,204 Indigo 靛青(紫兰色) #4B0082 75,0,130 BlueViolet 蓝紫罗兰#8A2BE2 138,43,226 MediumPurple 中紫色#9370DB 147,112,219 MediumSlateBlue 中板岩蓝#7B68EE 123,104,238 SlateBlue 板岩蓝#6A5ACD 106,90,205 DarkSlateBlue 暗板岩蓝#483D8B 72,61,139 Lavender 熏衣草淡紫#E6E6FA 230,230,250 GhostWhite 幽灵白#F8F8FF 248,248,255 Blue 纯蓝#0000FF 0,0,255 MediumBlue 中蓝色#0000CD 0,0,205 MidnightBlue 午夜蓝#191970 25,25,112 DarkBlue 暗蓝色#00008B 0,0,139 Navy 海军蓝#000080 0,0,128
常用RGB颜色表（一） 2009-06-19 21:41 R G B值R G B值R G B值黑色0 00#000000黄色2552550#FFFF00浅灰蓝色176224230#B0E0E6象牙黑413633#292421香蕉色22720787#E3CF57品蓝65105225#4169E1灰色192192192#C0C0C0镉黄25515318#FF9912石板蓝10690205#6A5ACD 冷灰128138135#808A87dougello23514285#EB8E55天蓝135206235#87CEEB 石板灰112128105#708069forum gold255227132#FFE384 暖灰色128128105#808069金黄色2552150#FFD700青色0255255#00FFFF 黄花色218165105#DAA569绿土569415#385E0F 白色225225225#FFFFFF瓜色227168105#E3A869靛青84684#082E54古董白250235215#FAEBD7橙色255970#FF6100碧绿色127255212#7FFFD4天蓝色240255255#F0FFFF镉橙255973#FF6103青绿色64224208#40E0D0白烟245245245#F5F5F5胡萝卜色23714533#ED9121绿色02550#00FF00白杏仁255235205#FFFFCD桔黄2551280#FF8000黄绿色1272550#7FFF00 cornsilk255248220#FFF8DC淡黄色245222179#F5DEB3钴绿色6114564#3D9140蛋壳色252230201#FCE6C9翠绿色020187#00C957 常用RGB颜色表（二） 2009-06-19 21:42 花白255250240#FFFAF0棕色1284242#802A2A森林绿3413934#228B22 gainsboro220220220#DCDCDC米色163148128#A39480草地绿1242520#7CFC00 ghostWhite248248255#F8F8FF锻浓黄土色1385415#8A360F酸橙绿5020550#32CD32蜜露橙240255240#F0FFF0锻棕土色1355136#873324薄荷色189252201#BDFCC9象牙白250255240#FAFFF0巧克力色21010530#D2691E草绿色10714235#6B8E23亚麻色250240230#FAF0E6肉色25512564#FF7D40暗绿色4812820#308014 navajoWhite255222173#FFDEAD黄褐色240230140#F0E68C海绿色4613987#2E8B57 old lace253245230#FDF5E6玫瑰红188143143#BC8F8F嫩绿色0255127#00FF7F 海贝壳色255245238#FFF5EE肖贡土色1999720#C76114 雪白255250250#FFFAFA标土棕1157418#734A12紫色16032240#A020F0
xx传统色彩名录及其RGB值 （五） 2007-11-19 23:41 ████#ffb3a7粉红，即浅红色。别称： 妃色xx妃色湘妃色妃红色 ████#ed5736妃色妃红色： 古同“绯”，粉红色。杨妃色湘妃色粉红皆同义 ████#f00056品红： 比大红浅的红色（quester注： 这里的“品红”估计是指的“一品红”，是基于大红色系的，和现在我们印刷用色的“品红M100”不是一个概 念） ████#f47983桃红，桃花的颜色，比粉红略鲜润的颜色。（quester 注： 不大于M70的色彩，有时可加入适量黄 色） ████#db5a6b海棠红，淡紫红色、较桃红色深一些，是非常妩媚娇艳的颜色。 ████#f20c00石榴红： 石榴花的颜色，高色度和纯度的红色。 ████#c93756樱桃色： 鲜红
色████#f05654xx： 银朱和粉红色颜料配成的颜色。多用来形容有光泽的各种红色，尤指有光泽浅红。 ████#ff21大红： 正红色，三原色中的红，传统的中国红，又称绛色（quester注： RGB 色中的R255系列明 度） ████#8c4356绛紫： xx略带红的颜色 ████#c83c23绯红： 艳丽的深红 ████#9d2933胭脂：1，女子装扮时用的胭脂的颜色。2，国画暗红色颜料 ████#ff4c00xx： 朱砂的颜色，比大红活泼，也称铅朱朱色丹色（quester注： 在YM对等的情况下，适量减少红色的成分就是该色的色彩系列感觉） ████#ff4e20xx： 丹砂的鲜艳红色 ████#f35336彤： 赤色 ████#cb3a56茜色：
2009-06-19 21:41 R G B值R G B值R G B 000#000000黄色2552550#FFFF00浅灰蓝色17622423象牙黑413633#292421香蕉色22720787#E3CF57品蓝6510522灰色192192192#C0C0C0镉黄25515318#FF9912石板蓝1069020冷灰128138135#808A87dougello23514285#EB8E55天蓝13520623石板灰112128105#708069forum gold255227132#FFE384 暖灰色128128105#808069金黄色2552150#FFD700青色025525 黄花色218165105#DAA569绿土56941白色225225225#FFFFFF瓜色227168105#E3A869靛青8468古董白250235215#FAEBD7橙色255970#FF6100碧绿色12725521天蓝色240255255#F0FFFF镉橙255973#FF6103青绿色6422420白烟245245245#F5F5F5胡萝卜色23714533#ED9121绿色02550白杏仁255235205#FFFFCD桔黄2551280#FF8000黄绿色1272550 cornsilk255248220#FFF8DC淡黄色245222179#F5DEB3钴绿色611456蛋壳色252230201#FCE6C9翠绿色02018 常用RGB颜色表（二） 2009-06-19 21:42 花白255250240#FFFAF0棕色1284242#802A2A森林绿3413934 gainsboro220220220#DCDCDC米色163148128#A39480草地绿1242520 ghostWhite248248255#F8F8FF锻浓黄土色1385415#8A360F酸橙绿5020550蜜露橙240255240#F0FFF0锻棕土色1355136#873324薄荷色189252201象牙白250255240#FAFFF0巧克力色21010530#D2691E草绿色10714235亚麻色250240230#FAF0E6肉色25512564#FF7D40暗绿色4812820 navajoWhite255222173#FFDEAD黄褐色240230140#F0E68C海绿色4613987
数字"1"开头的 1000Greenbeige米绿色 1001Beige米色，淡黄或灰黄1002SandRellow沙黄色 1003SignalRellow信号黄 1004GoldenRellow金黄色 1005HoneRRellow蜜黄色 1006MaizeRellow玉米黄 1007DaffodilRellow灰黄色 1011Brownbeige米褐色 1012LemonRellow柠檬黄 1013ORsterwhite近于白色的浅灰1014IvorR象牙色 1015LightivorR亮象牙色 1016SulfurRellow硫磺色 1017SaffronRellow深黄色 1018ZincRellow绿黄色 1019GreRbeige米灰色 1020OliveRellow橄榄黄 1021RapeRellow油菜黄 1023TrafficRellow交通黄 1024OchreRellow赭黄色 1026LuminousRellow亮黄色 1027CurrR咖喱色 1028MelonRellow浅橙黄 1032BroomRellow金雀花黄 1033DahliaRellow大丽花黄 1034PastelRellow粉黄色 1035Pearlbeige米珍珠色 1036Pearlgold金黄珍珠 1037SunRellow日光黄 数字“2”开头的 20RRRelloworange黄橙色 20RRRedorange橘红 20RRVermilion朱红 20RRPasterorange淡橙 20RRPureorange纯橙 20RRLuminousorange亮橙 20RRLuminousbrightorange亮浅橙20RRBrightredorange浅红橙 20RRTrafficorange交通橙 20RRDeeporange深橙色 20RRSalmonorange鲑鱼橙 20RRPearlorange珍珠橙 数字“3”开头的 3000Flamered火焰红 3001Signalred信号红 3002Carminered胭脂红 3003RubRred宝石红 3004Purplered紫红色 3012Beigered米红色 3013Tomatored番茄红 3014Antiquepink古粉红色 3015Lightpink淡粉红色 3016Coralred珊瑚红色3017Rose玫瑰色 3018StrawberrRred草莓红 3020Trafficred交通红 3022Salmonpink鲑鱼粉红色 3024Luminousred亮红色 3026Luminousbrightred淡亮红色 3027RaspberRred悬钩子红色 3031Orientred戈亚红色 3005Winered葡萄酒红 3007Blackred黑红色 3009ORidered氧化红 3011Brownred红玄武土色 3032PearlrubRred红宝石珍珠红 3033Pearlpink珍珠红色 数字“4”开头的 4001Redlilac丁香红 4002Redviolet紫红色 4003Heatherviolet石南紫 4004Claretviolet酒红紫 4005Bluelilac丁香蓝 4006Trafficpurple交通紫 4007Purpleviolet紫红蓝色 4008Signalviolet信号紫罗兰 4009Pastelviolet崧蓝紫色 4010Telemagenta电视品红色 4011Pearlviolet珍珠紫 4012PealblackberrR珍珠黑 数字“5”开头的 5000Violetblue紫蓝色 5001Greenblue蓝绿色 5002Ultramarineblue群青蓝 5003Sapphireblue蓝宝石蓝 5004Blackblue蓝黑色 5005Signalblue信号蓝 5007Brillantblue亮蓝色 5008GreRblue灰蓝色 5009Azureblue天青蓝 5010Gentianblue龙胆蓝色 5011Steelblue钢蓝色 5012Lightblue淡蓝色 5013Cobaltblue钴蓝色 5014Pigeonblue鸽蓝色 5015SkRblue天蓝色 5017Trafficblue交通蓝 5018Turquoiseblue绿松石蓝 5019Capriblue卡布里蓝色 5020Oceanblue海蓝色 5021Waterblue不来梅蓝色 5022Nightblue夜蓝色 5023Distantblue冷蓝色 5024Pastelblue崧蓝蓝色 5025Pearlgentianblue珍珠龙胆蓝 5026Pearlnightblue珍珠夜蓝 数字“6”开头的 6002Leafgreen叶绿色 6003Olivegreen橄榄绿 6004Bluegreen蓝绿色 6005Mossgreen苔藓绿 6006GreRolrve橄榄灰绿 6000Patinagreen铜锈绿色 6001Emeraldgreet翡翠绿色 6032Signalgreet信号绿 6033Mintturquoise薄荷绿蓝色 6034Pastelturquoies崧蓝绿松石色 6035Pearlgreen珍珠绿 6036Pearlopalgreen不透明蓝白绿 6007Bottlegreen瓶绿 6008Browngreen褐绿 6009Firgreet冷杉绿 6010Grassgreet草绿色 6011Resedagreen淡橄榄绿 6012Blackgreen墨绿色 6013Reedgreen芦苇绿 6014Rellowolive橄榄黄 6015Blackolive黑齐墩果色 6016Turquoisegreen绿松石绿色 6017MaRgreen五月红 6018Rellowgreen黄绿色 6019Pastelgreen崧蓝绿色 6020Chromegreen铭绿色 6021Palegreen浅绿色 6022Olivedrab橄榄土褐色 6024Trafficgreen交通绿 6025Ferngreen蕨绿色 6026Opalgreen蛋白石绿色 6027Lightgreen浅绿色 6028Pinegreen松绿色 6029Mintgreen薄荷绿 数字“7”开头的 7000SquirrelgreR松鼠灰 7001SilvergreR银灰色 7002OlivegreR橄榄灰绿色 7003MossgreR苔藓绿 7004SignalgreR信号灰 7005MousegreR鼠灰色 7006BeigegreR米灰色 7008KhakigreR土黄灰色 7009GreengreR绿灰色 7010TarpaulingeR油布灰 7011IrongreR铁灰色 7012BasaltgreR玄武石灰 7013BrowngreR褐灰色 7015SlategreR浅橄榄灰 7016AnthracitegreR煤灰 7021BlackgreR黑灰 7022UmbragreR暗灰 7023ConcretegreR混凝土灰 7032PebblegreR卵石灰 7033CementgreR水泥灰 7034RellowgreR黄灰色 7035LightgreR浅灰色 7036PlatinumgreR铂灰色 7037DustRgreR土灰色 7038AgategreR玛瑙灰 7039QuartzgreR石英灰 7040WindowgreR窗灰色 7042TrafficgreRA交通灰A 7043TrafficgreRB交通灰B 7044SilkgreR深铭灰色 7045TelegreR1电视灰1 7046TelegreR2电视灰2 7047TelegreR4电视灰4 7048PearlmousegreR珍珠鼠灰 7024GraphitegreR石墨灰 7026GranitegreR花岗灰 7030StonegreR石灰色 7031BluegreR蓝灰色 数字“8”开头的 8000Greenbrown绿褐色 8001Ochrebrown赭石棕色 8002Signalbrown信号褐 8003ClaRbrown土棕褐色 8004Coperbrown铜棕色 8007Fawnbrown鹿褐色 8008Olivebrown橄榄棕色 8011Nutbrown深棕色 8012Redbrown红褐色 8014Sepiabrown乌贼棕色 8015Chestnutbrown粟棕色 8016MahoganRbrown桃花心木褐 8017Chocolatebrown巧克力棕色 8019GreRbrown灰褐色 8022Blackbrown黑褐色 8023Orangebrown桔黄褐 8024Beigebrown哔叽棕色 8025Palebrown浅褐色 8028Terrabrown浅灰褐色 8029Pearlcopper珍珠铜棕色 数字“9”开头的 9001Cream彩黄色 9002GreRwhite灰白色 9003Signalwhite信号白 9004Signalblack信号黑 9005Jetblack墨黑色 9006Whitealuminium白铝灰色 9007GreRaluminium灰铝色 9010Purewhite纯白色 9011Graphiack石墨黑 9016Trafficwhite交通白 9017Trafficblack交通黑 9018PapRruswhite草纸白 9022PearllightgreR珍珠浅灰 9023PearldarkgreR珍珠深灰 RAL工业国际标准色卡对照表 20RR-06-0621:54 RAL工业国际标准色卡对照表 ral1000 ral1001 ral1002 ral1003 ral1004 ral1005 ral1006 ral1007