An Improved Contextual Classification Method of Point Cloud

doi:10.11947/j.AGCS.2017.20160096

Abstract

Abstract: To address the lacking of effectively utilization of nonlocal spatial context information on complex scene when classifying point cloud, an improved contextual classification method is proposed for point cloud with linear distribution and uneven density. Firstly, the local point cloud features and interaction spatial context were estimated based on the curvature based adaptive neighborhoods. Then, the supervoxel based distribution spatial context was extracted from point cloud. Finally, the point cloud classification was achieved automatically via higher-order conditional random field, which overcomes the limitation of local feature based point cloud classification. The experimental results show that the proposed method is able to improve the accuracy of point cloud classification effectively.

Key words: point cloud, classification, spatial context, adaptive neighborhood, CRF

CLC Number:

P237

HE Elong, WANG Hongping, CHEN Qi, LIU Xiuguo. An Improved Contextual Classification Method of Point Cloud[J]. Acta Geodaetica et Cartographica Sinica, 2017, 46(3): 362-370.

References

[1] WEINMANN M, JUTZI B, HINZ S, et al. Semantic Point Cloud Interpretation Based on Optimal Neighborhoods, Relevant Features and Efficient Classifiers[J]. ISPRS Journal of Photogrammetry and Remote Sensing, 2015, 105:286-304.
[2] DEMANTKÉ J, VALLET B, PAPARODITIS N. Streamed Vertical Rectangle Detection in Terrestrial Laser Scans for Facade Database Production[C]//ⅩⅫ Congress of the International Society of Photogrammetry and Remote Sensing. Melbourne, Australia:[s.n.], 2012:99-104.
[3] XU Lijun, KONG Deming, LI Xiaolu. On-the-fly Extraction of Polyhedral Buildings From Airborne LiDAR Data[J]. IEEE Geoscience and Remote Sensing Letters, 2014, 11(11):1946-1950.
[4] SERNA A, MARCOTEGUI B. Detection, Segmentation and Classification of 3D Urban Objects Using Mathematical Morphology and Supervised Learning[J]. ISPRS Journal of Photogrammetry and Remote Sensing, 2014, 93:243-255.
[5] YU Yongtao, LI J, GUAN Haiyan, et al. Semiautomated Extraction of Street Light Poles from Mobile LiDAR Point-Clouds[J]. IEEE Transactions on Geoscience and Remote Sensing, 2015, 53(3):1374-1386.
[6] 方莉娜, 杨必胜. 车载激光扫描数据的结构化道路自动提取方法[J]. 测绘学报, 2013, 42(2):260-267. FANG Lina, YANG Bisheng. Automated Extracting Roads from Mobile Laser Scanning Point Clouds[J]. Acta Geodaetica et Cartographica Sinica, 2013, 42(2):260-267.
[7] MUSIALSKI P, WONKA P, ALIAGA D G, et al. A Survey of Urban Reconstruction[J]. Computer Graphics Forum, 2013, 32(6):146-177.
[8] 董震, 杨必胜. 车载激光扫描数据中多类目标的层次化提取方法[J]. 测绘学报, 2015, 44(9):980-987. DOI:10.11947/j.AGCS.2015.20140339. DONG Zhen, YANG Bisheng. Hierarchical Extraction of Multiple Objects from Mobile Laser Scanning Data[J]. Acta Geodaetica et Cartographica Sinica, 2015, 44(9):980-987. DOI:10.11947/j.AGCS.2015.20140339.
[9] 郭波, 黄先锋, 张帆, 等. 顾及空间上下文关系的JointBoost点云分类及特征降维[J]. 测绘学报, 2013, 42(5):715-721. GUO Bo, HUANG Xianfeng, ZHANG Fan, et al. Points Cloud Classification Using Joint Boost Combined with Contextual Information for Feature Reduction[J]. Acta Geodaetica et Cartographica Sinica, 2013, 42(5):715-721.
[10] WEINMANN M, SCHMIDT A, MALLET C, et al. Contextual Classification of Point Cloud Data by Exploiting Individual 3D Neigbourhoods[J]. ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences, 2015, 2(3):271-278.
[11] WEINMANN M, JUTZI B, MALLET C. Semantic 3D Scene Interpretation:A Framework Combining Optimal Neighborhood Size Selection with Relevant Features[J]. ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences, 2014, 2(3):181-188.
[12] LALONDE J F, UNNIKRISHNAN R, VANDAPEL N, et al. Scale Selection for Classification of Point-Sampled 3D Surfaces[C]//Proceedings of the Fifth International Conference on 3-D Digital Imaging and Modeling. Ottawa, Ontario:IEEE, 2005:285-292.
[13] DEMANTKÉ J, MALLET C, DAVID N, et al. Dimensionality Based Scale Selection in 3D Lidar Point Clouds[J]. The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, 2011, 38(12):97-102.
[14] 杨必胜, 董震, 魏征, 等. 从车载激光扫描数据中提取复杂建筑物立面的方法[J]. 测绘学报, 2013, 42(3):411-417. YANG Bisheng, DONG Zhen, WEI Zheng, et al. Extracting Complex Building Facades from Mobile Laser Scanning Data[J]. Acta Geodaetica et Cartographica Sinica, 2013, 42(3):411-417.
[15] ANGUELOV D, TASKARF B, CHATALBASHEV V, et al. Discriminative Learning of Markov Random Fields for Segmentation of 3D Scan Data[C]//Proceedings of 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition. San Diego, CA:IEEE, 2005, 2:169-176.
[16] NIEMEYER J, ROTTENSTEINER F, SOERGEL U. Contextual Classification of LiDAR Data and Building Object Detection in Urban Areas[J]. ISPRS Journal of Photogrammetry and Remote Sensing, 2014, 87:152-165.
[17] MUNOZ D, BAGNELL J A, VANDAPEL N, et al. Contextual Classification with Functional Max-Margin Markov Networks[C]//Proceedings of 2009 IEEE Conference on Computer Vision and Pattern Recognition. Miami, FL:IEEE, 2009:975-982.
[18] LALONDE J F, VANDAPEL N, HUBER D F, et al. Natural Terrain Classification Using Three-dimensional LiDAR Data for Ground Robot Mobility[J]. Journal of Field Robotics, 2006, 23(10):839-861.
[19] WEINMANN M, JUTZI B, MALLET C. Feature Relevance Assessment for the Semantic Interpretation of 3D Point Cloud Data[J]. ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences, 2013, 1(2):313-318.
[20] PAPON J, ABRAMOV A, SCHOELER M, et al. Voxel Cloud Connectivity Segmentation-Supervoxels for Point Clouds[C]//Proceedings of the 2013 IEEE Conference on Computer Vision and Pattern Recognition. Portland, OR:IEEE, 2013:2027-2034.
[21] KOHLI P, KUMAR M P, TORR P H S. P3 & Beyond:Solving Energies with Higher Order Cliques[C]//Proceedings of 2007 IEEE Conference on Computer Vision and Pattern Recognition. Minneapolis, MN:IEEE, 2007:1-8.
[22] LAFFERTY J D, MCCALLUM A, PEREIRA F C N. Conditional Random Fields:Probabilistic Models for Segmenting and Labeling Sequence Data[C]//Proceedings of the Eighteenth International Conference on Machine Learning. San Francisco:ACM, 2001:282-289.
[23] KOHLI P, LADICKY L, TORR P H S. Robust Higher Order Potentials for Enforcing Label Consistency[J]. International Journal of Computer Vision, 2009, 82(3):302-324.
[24] TASKAR B, CHATALBASHEV V, KOLLER D. Learning Associative Markov Networks[C]//Proceedings of the Twenty-First International Conference on Machine Learning. New York, NY:ACM, 2004:102.
[25] BOYKOV Y, KOLMOGOROV V. An Experimental Comparison of Min-Cut/Max-Flow Algorithms for Energy Minimization in Vision[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2004, 26(9):1124-1137.