基于信息论的KL-Reg点云配准算法

秦红星; 徐雷

doi:10.11999/JEIT141248

基于信息论的KL-Reg点云配准算法

doi: 10.11999/JEIT141248

秦红星¹,
徐雷¹

基金项目:

国家自然科学基金青年科学基金(61100113)，国家教育部留学归国基金教外司留[2012]940号，重庆市首批青年骨干教师项目(渝教人(2011)31号)，重庆市基础与前沿研究计划项目(cstc2013jcyjA 40062)，重庆邮电大学学科引进人才基金(A2010-12)和重庆市研究生科研创新项目(CYS14142)资助课题

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出版历程
- 收稿日期: 2014-09-25
- 修回日期: 2015-02-27
- 刊出日期: 2015-06-19

Information Theory Based KL-Reg Point Cloud Registration

Qin Hong-xing¹,
Xu Lei¹

摘要

摘要: 针对含有高噪声、体外点及不完整点云数据的配准失效问题，该文提出以信息论为理论基础，相对熵度量点云相似度的KL-Reg算法。该算法不需要显式地建立对应关系，首先将点云数据建模为高斯混合模型，然后用相对熵度量高斯混合模型间的分布距离，最后通过最小化分布距离计算模型变换。实验结果表明所提的KL-Reg算法配准精度高、稳定性强。
- 机器视觉 /
- 点云配准 /
- KL散度 /
- 高斯混合模型
Abstract: The registration of point clouds with high noises, outliers and missing data will be failure because the correspondence between point clouds is inaccurate. This paper proposes a information theory based point cloud registration method called KL-Reg algorithm without building correspondence. The method represents the point cloud with Gaussian mixture model, then computes the transformation through minimizing the KL divergence without build explicit correspondence. Experimental results show that KL-Reg algorithm is precise and stable.
- Machine vision /
- Point clouds registration /
- KL-divergence /
- Gaussian mixture model

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参考文献(22)

Zitova B and Flusser J. Image registration methods: a survey[J]. Image and Vision Computing, 2003, 21(11): 977-1000.

Lian Z, Godil A, Bustos B, et al.. A comparison of methods for non-rigid 3D shape retrieval[J]. Pattern Recognition, 2013, 46(1): 449-461.

Liu M, Vemuri B C, Amari S I, et al.. Shape retrieval using hierarchical total bregman soft clustering[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2012, 34(12): 2407-2419.

Besl P J and McKay N D. Method for registration of 3-D shapes[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 1992, 14(2): 586-606.

Tagliasacchi A, Bouaziz S, and Pauly M. Sparse iterative closest point[J]. Computer Graphics Forum, 2013, 32(5): 113-123.

Tam G K L, Cheng Z Q, Lai Y K, et al.. Registration of 3D point clouds and meshes: a survey from rigid to nonrigid[J]. IEEE Transactions on Visualization and Computer Graphics, 2013, 19(7): 1199-1217.

Jian B and Vemuri B C. Robust point set registration using gaussian mixture models[J]. IEEE Transactions on Pattern

Analysis and Machine Intelligence, 2011, 33(8): 1633-1645.

Myronenko A and Song X. Point set registration: coherent point drift[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2010, 32(12): 2262-2275.

Tsin Y and Kanade T. A correlation-based approach to robust point set registration[C]. Computer Vision-ECCV 2004. Springer Berlin Heidelberg, Prague, 2004: 558-569.

Granger S and Pennec X. Multi-scale EM-ICP: a fast and robust approach for surface registration[C]. Computer VisionECCV 2002, Springer Berlin Heidelberg, Copenhagen, 2002: 418-432.

Hermans J, Smeets D, Vandermeulen D, et al.. Robust point set registration using EM-ICP with information-theoretically optimal outlier handling[C]. IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Colorado, USA, 2011: 2465-2472.

Lipman Y, Yagev S, Poranne R, et al.. Feature matching with bounded distortion[J]. ACM Transactions on Graphics, 2014, 33(3), DOI: 10.1145/2602142.

Paulus S, Dupuis J, Mahlein A K, et al.. Surface feature based classification of plant organs from 3D laserscanned point clouds for plant phenotyping[J]. BMC Bioinformatics, 2013, 14(1), DOI: 10.1186/147-2105-14-238.

Zeng Y, Wang C, Gu X, et al.. A generic deformation model for dense non-rigid surface registration: a higher-order MRF- based approach[C]. IEEE International Conference on Computer Vision (ICCV), Sydney, Australia, 2013: 3360-3367.

Hou T and Qin H. Robust dense registration of partial nonrigid shapes[J]. IEEE Transactions on Visualization and Computer Graphics, 2012, 18(8): 1268-1280.

Lombaert H, Grady L, Polimeni J R, et al.. FOCUSR: feature oriented correspondence using spectral regularization(a method for precise surface matching[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, Australia, 2013, 35(9): 2143-2160.

Chang W and Zwicker M. Automatic registration for articulated shapes[J]. Computer Graphics Forum, 2008, 27(5): 1459-1468.

Bronstein M M and Kokkinos I. Scale-invariant heat kernel signatures for non-rigid shape recognition[C]. IEEE Conference on Computer Vision and Pattern Recognition (CVPR), San Francisco, 2010: 1704-1711.

Sun J, Ovsjanikov M, and Guibas L. A concise and provably informative multi-scale signature based on heat diffusion[J]. Computer Graphics Forum, 2009, 28(5): 1383-1392.

Hershey J R and Olsen P A. Approximating the Kullback Leibler divergence between Gaussian mixture models[C]. IEEE International Conference on Acoustics, Speech and Signal Processing, Honolulu, 2007(4): 317-320.

Karaboga D, Gorkemli B, Ozturk C, et al.. A comprehensive survey: artificial bee colony (ABC) algorithm and applications[J]. Artificial Intelligence Review, 2014, 42(1): 21-57.

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