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Implicit Surface Reconstruction with Radial Basis

Implicit Surface Reconstruction with Radial Basis
Implicit Surface Reconstruction with Radial Basis

J. Braz et al. (Eds.): VISIGRAPP 2007, CCIS 21, pp. 5–12, 2008.

? Springer-Verlag Berlin Heidelberg 2008

Implicit Surface Reconstruction with Radial Basis

Functions

Jun Yang 1, Zhengning Wang 2, Changqian Zhu 2, and Qiang Peng 2

1

School of Mechanical & Electrical Engineering Lanzhou Jiaotong

University, Lanzhou, Gansu 730070, China

2 School of Information Science & Technology Southwest Jiaotong

University, Chengdu, Sichuan 610031, China yangj@https://www.doczj.com/doc/2a18696214.html,, {znwang,cqzhu,pqiang}@https://www.doczj.com/doc/2a18696214.html, Abstract. This paper addresses the problem of reconstructing implicit function from point clouds with noise and outliers acquired with 3D scanners. We intro-duce a filtering operator based on mean shift scheme, which shift each point to local maximum of kernel density function, resulting in suppression of noise with different amplitudes and removal of outliers. The “clean” data points are then divided into subdomains using an adaptive octree subdivision method, and a local radial basis function is constructed at each octree leaf cell. Finally, we blend these local shape functions together with partition of unity to approximate the entire global domain. Numerical experiments demonstrate robust and high quality performance of the proposed method in processing a great variety of 3D reconstruction from point clouds containing noise and outliers.

Keywords: Filtering, space subdivision, radial basis function, partition of unity. 1 Introduction

The interest for point-based surface has grown significantly in recent years in com-puter graphics community due to the development of 3D scanning technologies, or the riddance of connectivity management that greatly simplifies many algorithms and data structures. Implicit surfaces are an elegant representation to reconstruct 3D sur-faces from point clouds without explicitly having to account for topology issues. However, when the point sets data generated from range scanners (or laser scanners) contain large noise, especially outliers, some established methods often fail to recon-struct surfaces or real objects.

There are two major classes of surface representations in computer graphics: para-metric surfaces and implicit surfaces. A parametric surface [1, 2] is usually given by a function f (s , t ) that maps some 2-dimensional (maybe non-planar) parameter domain ? into 3-space while an implicit surface typically comes as the zero-level isosurface of a 3-dimensional scalar field f (x , y , z ). Implicit surface models are popular since they can describe complex shapes with capabilities for surface and volume modeling and complex editing operations are easy to perform on such models. Moving least square (MLS) [3-6] and radial basis function (RBF) [7-15] are two popular 3D im-plicit surface reconstruction methods.

6 J. Yang et al.

Recently, RBF attracts more attention in surface reconstruction. It is identified as one of most accurate and stable methods to solve scattered data interpolation prob-lems. Using this technique, an implicit surface is constructed by calculating the weights of a set of radial basis functions such they interpolate the given data points. From the pioneering work [7, 8] to recent researches, such as compactly-supported RBF [9, 10], fast RBF [11-13] and multi-scale RBF [14, 15], the established algo-rithms can generate more and more faithful models of real objects in last twenty years, unfortunately, most of them are not feasible for the approximations of unorgan-ized point clouds containing noise and outliers.

In this paper, we describe an implicit surface reconstruction algorithm for noise scattered point clouds with outliers. First, we define a smooth probability density kernel function reflecting the probability that a point p is a point on the surface S sampled by a noisy point cloud. A filtering procedure based on mean shift is used to move the points along the gradient of the kernel functions to the maximum probability positions. Second, we reconstruct a surface representation of “clean” point sets im-plicitly based on a combination of two well-known methods, RBF and partition of unity (PoU). The filtered domain of discrete points is divided into many subdomians by an adaptively error-controlled octree subdivision, on which local shape functions are constructed by RBFs. We blend local solutions together using a weighting sum of local subdomains. As you will see, our algorithm is robust and high quality.

2 Filtering

2.1 Covariance Analysis

Before introducing our surface reconstruction algorithm, we describe how to perform eigenvalue decomposition of the covariance matrix based on the theory of principal component analysis (PCA) [24], through which the least-square fitting plane is de-fined to estimate the kernel-based density function.

Given the set of input points ?={p i }i ?[1,L ], p i ? R 3, the weighted covariance matrix

C for a sample point p i ? ? is determined by

()()()

T 1L h j i j i j i j =???Ψ?∑=C p p p p p p , (1) where i p is the centroid of the neighborhood of p i , Ψ is a monotonically decreasing weight function, and h is the adaptive kernel size for the spatial sampling density. Consider the eigenvector problem

l l l λ?=?C e e . (2)

Since C is symmetric and positive semi-define, all eigenvalues λl are real-valued and the eigenvectors e l form an orthogonal frame, corresponding to the principal compo-nents of the local neighborhood.

Assuming λ0≤λ1≤λ2, it follows that the least square fitting plane H(p ): ()0i 0??=p p e through i p minimizes the sum of squared distances to the neighbors

of p i . Thus e 0 approximates the surface normal n i at p i , i.e., n i = e 0. In other words, e 1 and e 2 span the tangent plane at p i .

Implicit Surface Reconstruction with Radial Basis Functions 7

2.2 Mean Shift Filtering

Mean shift [16, 17] is one of the robust iterative algorithms in statistics. Using this algorithm, the samples are shifted to the most likely positions which are local maxima of kernel density function. It has been applied in many fields of image processing and visualization, such as tracing, image smoothing and filtering.

In this paper, we use a nonparametric kernel density estimation scheme to estimate an unknown density function g (p ) of input data. A smooth kernel density function g (p ) is defined to reflect the probability that a point p ? R 3 is a point on the surface S sampled by a noisy point cloud ?. Inspired by the previous work of Schall et al. [21], we measure the probability density function g (p ) by considering the squared distance of p to the plane H(p ) fitted to a spatial k -neighborhood of p i as

()()()()(){}

2

pro pro 111L L i i i i i i i i g g G ====Φ?????????∑∑p p p p p p p p n , (3) where Φi and G i are two monotonically decreasing weighting functions to measure the spatial distribution of point samples from spatial domain and range domain, which are more adaptive to the local geometry of the point model. The weight function could be either a Gaussian kernel or an Epanechnikov kernel. Here we choose Gaussian func-tion 22/2x e σ? . The p pro is an orthogonal projection of a certain sample point p on the least-square fitting plane. The positions p close to H(p ) will be assigned with a higher probability than the positions being more distant.

The simplest method to find the local maxima of (3) is to use a gradient-ascent process written as follows:

()()1L i i g g =?=?∑p p ()()()pro pro 21

2L i i i i i i i G h =?≈

Φ?????????∑p p p p p p n n . (4) Thus the mean shift vectors are determined as

()()()()()pro pro pro pro 11()L

L i i i i i i i i i i i m G G ==??=?Φ?????Φ??????????

∑∑p p p p p p p p n n p p p p . (5) Combining equations (4) and (5) we get the resulting iterative equations of mean shift filtering

1()j j i i m +=p p , o i i =p p , (6)

where j is the number of iteration. In our algorithm, g (p ) satisfies the following condi-tions

()()()()21121120,0g g g ?>???≥?≥p p p p p p p , (7)

thus g (p ) is a convex function with finite stable points in the set ()(){}

1

|i i i U g g =≥p p p resulting in the convergence of the series {},1,...,,1,2,...j i i L j ==p . Experiments show that we stop iterative process if 13510j j i i h +??≤×p p

is satisfied. Each sample usually converges in less than 8 iterations. Due to the clustering property of our method, groups of outliers usually converge to a set of single points sparsely distributed around the surface samples. These points can be characterized by a very low spatial sampling density compared to the surface samples. We use this criteria for the detec-tion of outliers and remove them using a simple threshold.

8 J. Yang et al.

3 Implicit Surface Reconstruction

3.1 Adaptive Space Subdivision

In order to avoid solving a dense linear system, we subdivide the whole input points filtered by mean shift into slightly overlapping subdomains. An adaptive octree-based subdivision method introduced by Ohtake et al. [18] is used in our space partition. We define the local support radius R =α d i for the cubic cells which are generated during the subdivision, d i is the length of the main diagonal of the cell. Assume each cell should contain points between T min and T max . In our implementation, α=0.6, T min =20 and T max =40 has provided satisfying results.

A local max-norm approximation error is estimated according to the Taubin dis-tance [19], ()()max /i i i i c R

f f ε?<=?p p p . (8) If the ε is greater than a user-specified threshold ε0, the cell is subdivided and a local neighborhood function f i is built for each leaf cell.

3.2 Estimating Local Shape Functions

Given the set of N pairwise distinct points ?={p i }i ?[1,N ], p i ?R 3, which is filtered by mean shift algorithm, and the set of corresponding values {v i }i ?[1,N ], v i ?R, we want to find an interpolation f : R 3→R such that

()i i f v =p . (9)

We choose the f (p ) to be a radial basis function of the form

()()()1N i i

i f ηω?==+?∑p p p p , (10)

where η(p )= ζk ηk (p ) with {ηk (p )}k ?[1,Q ] is a basis in the 3D null space containing all

real-value polynomials in 3 variables and of order at most m with {}

33m Q += depending on the choice of φ, φ is a basis function, ωi are the weights in real numbers, and | . | denotes the Euclidean norm.

There are many popular basis functions φ for use: biharmonic φ(r ) = r , triharmonic φ(r ) = r 3, multiquadric φ(r ) = (r 2+c 2)1/2, Gaussian φ(r ) = exp(-cr 2), and thin-plate spline φ(r ) = r 2log(r ), where r = |p -p i |.

As we have an under-determined system with N+Q unknowns and N equations, so-called natural additional constraints for the coefficient ωi are added in order to ensure orthogonality, so that

121110N N N i i

i Q i i i ωηωηωη=======∑∑∑ . (11)

The equations (9), (10) and (11) may be written in matrix form as

0??????=????????????T A ηωv η

0ζ, (12) where A =φ(|p i -p j |), i,j =1,…,N , η=ηk (p i ), i =1,…,N , k =1,…,Q , ω=ωi , i =1,…,N and ζ=ζk , k =1,…,Q . Solving the linear system (14) determines ωi and ζk , hence the f (p ).

Implicit Surface Reconstruction with Radial Basis Functions 9

Fig. 1. A set of locally defined functions are blent by the PoU method. The resulting function (red solid curve) is constructed from four local functions (thick dashed curves) with their associated weight functions (dashed dotted curves).

3.3 Partition of Unity

After suppressing high frequency noise and removing outliers, we divide the global domain ?={p i }i ?[1,N ] into M lightly overlapping subdomains {?i }i ?[1,M ] with i i Ω?Ω∪ using an octree-based space partition method. On this set of subdomains {?i }i ?[1,M ], we construct a partition of unity, i.e., a collection of non-negative functions {Λi }i ?[1,M ] with limited support and with ∑Λi =1 in the entire domain ?. For each subdomain ?i we construct a local reconstruction function f i based on RBF to interpolate the sam-pled points. As illustrated in Fig. 1, four local functions f 1(p ), f 2(p ), f 3(p ) and f 4(p ) are blended together by weight functions Λ1, Λ2, Λ3 and Λ4. The red solid curve is the final reconstructed function.

Now an approximation of a function f (p ) defined on ? is given by a combination of the local functions

()()()1M

i i i f f ==Λ∑p p p . (13)

The blending function is obtained from any other set of smooth functions by a nor-malization procedure

()()()i i j j

w w Λ=p p p . (14)

The weight functions w i must be continuous at the boundary of the subdomains ?i . Tobor et al. [15] suggested that the weight functions w i be defined as the composition of a distance function D i :R n →[0,1], where D i (p )=1 at the boundary of ?i and a decay function θ: [0,1]→[0,1], i.e. w i (p )= θ ? D i (p ). More details about D i and θ can be found in Tobor’s paper.

4 Applications and Results

All results presented in this paper are performed on a 2.8GHz Intel Pentium4 PC with 512M of RAM running Windows XP.

To visualize the resulting implicit surfaces, we used a pure point-based surface rendering algorithm such as [22] instead of traditionally rendering the implicit sur-faces using a Marching Cubes algorithm [23], which inherently introduces heavy topological constraints.

10 J. Yang et al.

Table 1. Computational time measurements for mean shift filtering and RBF+PoU surface reconstructing with error bounded at 10-5. Timings are listed as minutes:seconds.

model Bunny Dragon head Dragon

P input 362K 485K 2.11M

P filter 165K 182K 784K

T filter 9:07 13:26 41:17

T octree 0:02 0:04 0:10

T rec 0:39 0:51 3:42

(a) (b) (c)

Fig. 2. Comparison of implicit surface reconstruction based on RBF methods. (a) Input noisy point set of Stanford bunny (362K). (b) Reconstruction with Carr’s method [11]. (c) Reconstruction with our method in this paper.

(a) (b) (c) (d)

Fig. 3. Error threshold controls reconstruction accuracy and smoothness of the scanned dragon model consisting of 2.11M noisy points. (a) Reconstructing with error threshold at 8.4x10-4. (c) Reconstructing with error threshold at 2.1x10-5. (b) and (d) are close-ups of the red rectangle areas of (a) and (c) respectively.

Table 1 presents computational time measurements for filtering and reconstructing of three scan models, bunny, dragon head and dragon, with user-specified error threshold 10-5 in this paper. In order to achieve good effects of denoising we choose a large number of k-neighborhood for the adaptive kernel computation, however, more timings of filtering are spent . In this paper, we set k=200. Note that the filtered points are less than input noisy points due to the clustering property of our method.

In Fig. 2 two visual examples of the reconstruction by Carr’s method [11] and our algorithm are shown. Carr et al. use polyharmonic RBFs to reconstruct smooth, mani-fold surfaces from point cloud data and their work is considered as an excellent and successful research in this field. However, because of sensitivity to noise, the

Implicit Surface Reconstruction with Radial Basis Functions 11

reconstructed model in the middle of Fig. 2 shows spurious surface sheets. The qual-ity of the reconstruction is highly satisfactory, as be illustrated in the right of Fig. 2, since a mean shift operator is introduced to deal with noise in our algorithm.

For the purpose of illustrating the influence of error thresholds on reconstruction accuracy and smoothness, we set two different error thresholds on the reconstruction of the scanned dragon model, as demonstrated by Fig. 3.

5 Conclusions and Future Work

In this study, we have presented a robust method for implicit surface reconstruction from scattered point clouds with noise and outliers. Mean shift method filters the raw scanned data and then the PoU scheme blends the local shape functions defined by RBF to approximate the whole surface of real objects.

We are also investigating various other directions of future work. First, we are trying to improve the space partition method. We think that the Volume-Surface Tree [20], an alternative hierarchical space subdivision scheme providing efficient and accurate sur-face-based hierarchical clustering via a combination of a global 3D decomposition at coarse subdivision levels, and a local 2D decomposition at fine levels near the surface may be useful. Second, we are planning to combine our method with some feature ex-traction procedures in order to adapt it for processing very incomplete data. Acknowledgements. This work was supported by ‘Qing Lan’ Talent Engineering Funds by Lanzhou Jiaotong University.

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https://www.doczj.com/doc/2a18696214.html,/W9TPI/FD54DF81A4CCF4511BA1445C606DDBA2/Wi ndowsTechnicalPreview-x86-ZH-CN.iso 64位系统下载:https://www.doczj.com/doc/2a18696214.html,/W9TPI/FD54DF81A4CCF4511BA1445C606DDBA2/Wi ndowsTechnicalPreview-x64-ZH-CN.iso windows 8.1 Pro 专业版(VOL版)简体中文: 32位系统下载:ed2k://|file|cn_windows_8.1_professional_vl_with_update_x86_dvd_4048630.iso|308 6659584|41CAAB5DAC5643F6E0048576D609C19A|/ 64位系统下载:ed2k://|file|cn_windows_8.1_professional_vl_with_update_x64_dvd_4050293.iso|413 6626176|4D2363F9E06BFD50A78B1E6464702959|/ windows 8.1 Ent 企业版(VOL版)简体中文: 32位系统下载:ed2k://|file|cn_windows_8.1_enterprise_with_update_x86_dvd_4050277.iso|308994 4576|EEA035116570CFB1F6B6318559ED4A05|/ 64位系统下载:ed2k://|file|cn_windows_8.1_enterprise_with_update_x64_dvd_4048578.iso|414078 1568|8A3E13590D8A6EDF259574CB797FC75E|/

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windows正版系统+正版密钥

正版Windows 系统下载+正版密钥 2010-05-30 11:49 喜欢正版Windows 系统 这是我收集N天后的成果,正版的Windows系统真的很好用,支持正版!大家可以用激活工具激活!现将本收集的下载地址发布出来,希望大家多多支持! Windows 98 第二版(简体中文) 安装序列号:Q99JQ-HVJYX-PGYCY-68GM3-WXT68 安装序列号:Q4G74-6RX2W-MWJVB-HPXHX-HBBXJ 安装序列号:QY7TT-VJ7VG-7QPHY-QXHD3-B838Q Windows Millennium Edition (Window ME)(简体中文) 安装序列号:HJPFQ-KXW9C-D7BRJ-JCGB7-Q2DRJ 安装序列号:B6BYC-6T7C3-4PXRW-2XKWB-GYV33 安装序列号:K9KDJ-3XPXY-92WFW-9Q26K-MVRK8 Windows 2000 PRO SP4(简体中文) SerialNumber: BCJTW-2M9JH-M8HHT-KWWWM-3444Y MD5:599d35359320db8c58c7f55da11d3458 Windows XP with sp3 VOL 微软原版(简体中文) 文件名:zh-hans_windows_xp_professional_with_service_pack_3_x86_cd_vl_x14-74070.iso 大小: 630237184 字节

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Windows系统原版镜像下载地址

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安装Windows7原版系统教程

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弹出对话框里,选择你的u盘,点击写入。(写入过程u盘会全盘格式化,备份好有用的文件) 写入完成以后,你的u盘就是一个系统启动盘了。

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windows xp官方原版

1)近日,照牛排在野猪尖分享了雨林木风最后4个系统的下载地址,以及深度系统全系列MD5值及下载地址,喜欢雨林木风及深度的朋友,可以选择下载。尽管雨林木风和深度的系统经过优化会有一些优势,但毕竟是修改过的,有这样或那样的问题。我还是喜欢微软原版的系统,稳定。本文将分享微软原版Windows XP Pro With SP3 VOL MSDN简体中文专业版的下载地址、MD5值及序列号。 2)2008年5月2日,微软推出Windows XP Pro With SP3 VOL MSDN简体中文专业版,这是最经典也是我最喜爱的操作系统之一。我在MSDN(微软开发者网络)的网站上查到,Windows XP Pro With SP3 VOL MSDN简体中文版的全名叫Windows XP Professional with Service Pack 3 (x86) - CD VL (Chinese-Simplified),更详细的信息如下: 文件名: zh-hans_windows_xp_professional_with_service_pack_3_x86_cd_vl_x14-740 70.iso 文件大小:601 MB (630,237,184 字节) 发布日期 (UTC):5/2/2008 12:05:18 AM 上次更新日期 (UTC):2/19/2010 11:36:41 AM SHA1:D142469D0C3953D8E4A6A490A58052EF52837F0F MD5:E74D72F3D90456003E9E02BA0FB7DA61 3)微软官方原版Windows XP Pro With SP3 VOL MSDN简体中文专业版的迅雷下载地址为: thunder://QUFodHRwOi8vc29mdC51c2Fpa2EuY24vJUU2JTkzJThEJUU0JUJEJTlDJUU 3JUIzJUJCJUU3JUJCJTlGL3dpbmRvd3MvV2luZG93c1hQL1ZMLUltYWdlL01TRE4vemgt aGFuc193aW5kb3dzX3hwX3Byb2Zlc3Npb25hbF93aXRoX3NlcnZpY2VfcGFja18zX3g4N l9jZF92bF94MTQtNzQwNzAuaXNvWlo 电驴下载地址为: ed2k://|file|zh-hans_windows_xp_professional_with_service_pack_3_x86_ cd_vl_x14-74070%20(https://www.doczj.com/doc/2a18696214.html,).iso|630237184|EC51916C9D9B8B931195EE0D 6EE9B40E| 只要复制上面任何一个下载地址,并启动迅雷新建任务即可开始下载。这是一个ISO镜像文件,下载后,请用校验工具(点此下载)验证一下MD5值。你可以把镜像刻录到光盘,也可以解压后从硬盘安装。 4)至于微软原版Windows XP Pro With SP3 VOL MSDN简体中文专业版的序列号(CD-KEY,即密钥,俗称SN),我在用工行版的正版序列号: MRX3F-47B9T-2487J-KWKMF-RPWBY。你也可以尝试以下VOL版XP序列号: QC986-27D34-6M3TY-JJXP9-TBGMD(台湾交大学生版) HCQ9D-TVCWX-X9QRG-J4B2Y-GR2TT(https://www.doczj.com/doc/2a18696214.html,) DP7CM-PD6MC-6BKXT-M8JJ6-RPXGJ CM3HY-26VYW-6JRYC-X66GX-JVY2D

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