Computer Science ›› 2017, Vol. 44 ›› Issue (9): 11-16.doi: 10.11896/j.issn.1002-137X.2017.09.002

Previous Articles     Next Articles

Survey of 3D Object Recognition for Point Clouds

HAO Wen, WANG Ying-hui, NING Xiao-juan, LIANG Wei and SHI Zheng-hao   

  • Online:2018-11-13 Published:2018-11-13

Abstract: With the rapid development of 3D scanning technology,it is convenient to obtain point clouds of different scenes.Since point clouds are not influenced by light,shadows and textures,recognizing 3D object from scene point clouds has become a research hotspot of computer vision.This paper first summarized the 3D object recognition methods from point clouds in recent years.Then the advantages and disadvantages of the existing methods were discussed.Finally,the challenges and further research directions of object recognition were pointed out.

Key words: Point clouds,3D object recognition,Feature extraction,Graph matching

[1] SEIDENARI L,SERRA G,BAGDANOV A D,et al.Local pyramidal descriptors for image recognition[J].IEEE Transaction on Pattern Analysis and Machine Intelligence,2014,36(5):1033-1040.
[2] TIMOFTE R,ZIMMERMANN K,VAN GOOL L.Multi-viewtraffic sign detection,recognition,and 3d localisation[J].Machine Vision and Applications,2014,25(3):633-647.
[3] LU H,FENG X,LI X,et al.Superpixel level object recognition under local learning framework[J].Neurocomputing,2013,120:203-213.
[4] GUO Y,BENNAMOUN M,SOHEL F,et al.3D Object Recognition in Cluttered Scenes with Local Surface Features:A Survey[J].IEEE Transaction on Pattern Analysis and Machine Intelligence,2014,36(11):2270-2287.
[5] CHUA C S,JARVIS R.Point Signatures:A New Representation for 3D Object Recognition[J].International Journal of Computer Vision,1997,25(1):63-85.
[6] JOHNSON A E,HEBERT M.Using spin images for efficient object recognition in cluttered 3d scenes[J].IEEE Transaction on Pattern Analysis and Machine Intelligence,1999,21(5):433-449.
[7] DATE H,KANETA Y,HATSUKAIWA A,et al.Object Re-cognition in Terrestrial Laser Scan Data using Spin Images[J].Computer-Aided Design and Applications,2012,9(2):187-197.
[8] LIU Y,MA J,ZHAO J,et al.Three dimensional automatic tar- get recognition based on spin-images[J].Infrared and Laser Engineering,2012,41(2):543-548.(in Chinese) 刘瑶,马杰,赵季,等.基于自旋图的三维自动目标识别[J].红外与激光工程,2012,41(2):543-548.
[9] FROME A,HUBER D,KOLURI R,et al.Recognizing objects in range data using regional point descriptors[C]∥European Conference on Computer Vision.Springer Berlin Heidelberg,2004:224-237.
[10] TOMBARI F,SALTI S,DI STEFANO L.Unique shape context for 3D data description[C]∥Proceedings of the ACM workshop on 3D object retrieval.ACM,2010:57-62.
[11] ZHONG Y.Intrinsic shape signatures:A shape descriptor for 3d object recognition[C]∥ 2009 IEEE 12th International Con-ference on Computer Vision Workshops (ICCV Workshops).IEEE,2009:689-696.
[12] GUO Y,SOHEL F,BENNAMOUN M,et al.Rotational projection statistics for 3D local surface description and object recognition[J].International Journal of Computer Vision,2013,105(1):63-86.
[13] GUO Y,SOHEL F,BENNAMOUN M,et al.A novel local surface feature for 3D object recognition under clutter and occlusion[J].Information Sciences,2015,293:196-213.
[14] GUO Y,BENNAMOUN M,SOHEL F,et al.A comprehensive performance evaluation of 3D local feature descriptors[J].International Journal of Computer Vision,2016,116(1):66-89.
[15] TOMBARI F,SALTI S,DI STEFANO L.Unique signatures of histograms for local surface description[C]∥European Conference on Computer Vision.Springer Berlin Heidelberg,2010:356-369.
[16] SALTI S,TOMBARI F,DI STEFANO L.SHOT:unique signatures of histograms for surface and texture description[J].Computer Vision and Image Understanding,2014,125:251-264.
[17] TAATI B,GREENSPAN M.Local shape descriptor selectionfor object recognition in range data[J].Computer Vision and Image Understanding,2011,115(5):681-694.
[18] PRKAHYA S M,LIU B,LIN W.B-SHOT:A binary feature descriptor for fast and efficient keypoint matching on 3D point clouds[C]∥2015 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS).IEEE,2015:1929-1934.
[19] RUSU R B,BLODOW N,MARTON Z C,et al.Aligning point cloud views using persistent feature histograms[C]∥2008 IEEE/RSJ International Conference on Intelligent Robots and Systems.IEEE,2008:3384-3391.
[20] RUSU R B,BLODOW N,BEETZ M.Fast point feature histograms (FPFH) for 3D registration [C]∥IEEE International Conference on Robotics and Automation,2009(ICRA’09).IEEE,2009:3212-3217.
[21] BUSTOS B,KEIM D A,SAUPE D,et al.Feature-based similari-ty search in 3D object databases[J].ACM Computing Surveys (CSUR),2005,37(4):345-387.
[22] BRONSTEIN A M,BRONSTEIN M M,KIMMEL R.Numerical geometry of non-rigid shapes[M].New York:Springer,2008.
[23] RUSU R B,BRADSKI G,THIBAUX R,et al.Fast 3d recognition and pose using the viewpoint feature histogram[C]∥2010 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS).IEEE,2010:2155-2162.
[24] ALDOMA A,VINCZE M,BLODOW N,et al.CAD-model re-cognition and 6DOF pose estimation using 3D cues[C]∥2011 IEEE International Conference on Computer Vision Workshops (ICCV Workshops).IEEE,2011:585-592.
[25] ALDOMA A,TOMBARI F,RUSU R B,et al.OUR-CVFH-oriented,unique and repeatable clustered viewpoint feature histogram for object recognition and 6DOF pose estimation[M]∥ Pattern Recognition.Springer Berlin Heidelberg,2012:113-122.
[26] RUSU R B,HOLZBACH A,BEETZ M,et al.Detecting andsegmenting objects for mobile manipulation [C]∥2009 IEEE 12th International Conference on Computer Vision Workshops (ICCV Workshops).IEEE,2009:47-54.
[27] MARTON Z C,PANGERCIC D,BLODOW N,et al.Combined 2D-3D categorization and classification for multimodal perception systems[J].The International Journal of Robotics Research,2011,30(11):1378-1402.
[28] WOHLKINGER W,VINCZE M.Ensemble of shape functions for 3d object classification[C]∥2011 IEEE International Conference on Robotics and Biomimetics (ROBIO).IEEE,2011:2987-2992.
[29] SHANG L,GREENSPAN M.Real-time object recognition insparse range images using error surface embedding[J].International Journal of Computer Vision,2010,89(2/3):211-228.
[30] CHEN T,DAI B,LIU D,et al.Performance of global descriptors for velodyne-based urban object recognition[C]∥2014 IEEE Intelligent Vehicles Symposium Proceedings.IEEE,2014:667-673.
[31] DROST B,ULRICH M,NAVAB N,et al.Model globally,match locally:Efficient and robust 3D object recognition[C]∥ IEEE Conference on Computer Vision and Pattern Recognition (CVPR).IEEE,2010:998-1005.
[32] LAM J.Object recognition by registration of repeatable 3d in-terest segments[D].Queen’s University,Canada,2015.
[33] CHENG Y M,DING H X,WANG Y X,et al.Curved Object Recognition Based on Geometrical Features[J].Journal of Image and Graphics,2000,5(7):573-579.(in Chinese) 程义民,丁红侠,王以孝,等.基于几何特征的曲面物体识别[J].中国图像图形学报,2000,5(7):573-579.
[34] SCHNABEL R,WESSEL R,WAHL R,et al.Shape recognition in 3d point-clouds[C]∥The 16-th International Conference in Central Europe on Computer Graphics,Visualization and Computer Vision.2008.
[35] NIEUWENHUISEN M,STCKLER J,BERNER A,et al.Shape-primitive based object recognition and grasping[C]∥7th German Conference on Robotics,Proceedings of ROBOTIK 2012.VDE,2012:1-5.
[36] BERNER A,LI J,HOLZ D,et al.Combining contour and shape primitives for object detection and pose estimation of prefabricated parts[C]∥2013 IEEE International Conference on Image Processing.IEEE,2013:3326-3330.
[37] ZHAO Y,HE M,ZHAO H,et al.Computing object-based sa-liency in urban scenes using laser sensing[C]∥2012 IEEE International Conference on Robotics and Automation (ICRA).IEEE,2012:4436-4443.
[38] HAO W,WANG Y.Structure-based object detection from scenepoint clouds[J].Neurocomputing,2016,191:148-160.
[39] AGRAWAL A,NAKAZAWA A,T AKEMURA H.MMM-classification of 3D Range Data[C]∥IEEE International Conference on Robotics and Automation,2009(ICRA’09).IEEE,2009:2003-2008.
[40] CHENG J,XIANG Z Y,YU H B,et al.Real-time vehicle detection using 3D lidar under complex urban environment[J].Journal of Zhejiang University(Engineering Science),2014,48(12):2101-2106.(in Chinese) 程健,项志宇,于海滨,等.城市复杂环境下基于三维激光雷达实时车辆检测[J].浙江大学学报(工学报),2014,48(12):2101-2106.
[41] SUN J,LAI Z L.Airborne LiDAR feature selection for urbanclassification using random forests[J].Geomatics and Information Science of Wuhan University,2014,39(11):1310-1313.(in Chinese) 孙杰,赖祖龙.利用随机森林的城区机载LiDAR数据特征选择与分类[J].武汉大学学报,2014,39(11):1310-1313.
[42] NIEMEYER J,ROTTENSTEINER F,SOERGEL U.Condi-tional random fields for lidar point cloud classification in complex urban areas[J].ISPRS Annals of the Photogrammetry,Remote Sensing and Spatial Information Sciences,2012,1(3):263-268.
[43] ZHUANG Y,LIU Y,HE G,et al.Contextual classification of3D laser points with conditional random fields in urban environments[C]∥2015 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS).IEEE,2015:3908-3913.
[44] YAO W,WEI Y.Detection of 3-D individual trees in urban areas by combining airborne LiDAR data and imagery[J].IEEE Geoscience and Remote Sensing Letters,2013,10(6):1355-1359.
[45] NIEMEYER J,ROTTENSTEINER F,SOERGEL U.Contex-tual classification of lidar data and building object detection in urban areas[J].ISPRS Journal of Photogrammetry and Remote Sensing,2014,87:152-165.
[46] GOLOVINSKIN A,KIM V G,FUNKHOUSER T.Shape-based recognition of 3D point clouds in urban environments[C]∥2009 IEEE 12th International Conference on Computer Vision.IEEE,2009:2154-2161.
[47] VELIZHEV A,SHAPOVALOV R,SCHINDLER K.Implicitshape models for object detection in 3D point clouds[C]∥ISPRS Congress.2012:179-184.
[48] ZHAO H,LIU Y,ZHU X,et al.Scene understanding in a large dynamic environment through a laser-based sensing[C]∥2010 IEEE International Conference on Robotics and Automation (ICRA).IEEE,2010:127-133.
[49] ZHANG J X,LIN X G.Object-based classification of urban airborne lidar point clouds with multiple echoes using SVM[J].ISPRS Annals of Photogrametry,Remote Sensing and Spatial Information Sciences,2012,3:135-140.
[50] LEHTOMKI M,JAAKKOLA A,HYYPP J,et al.Objectclassification and recognition from mobile laser scanning point clouds in a road environment[J].IEEE Transactions on Geo-science and Remote Sensing,2016,54(2):1226-1239.
[51] AWAN S,MUHAMAD M,KUSEVIC K,et al.Object class re-cognition in mobile urban lidar data using global shape descriptors[C]∥2013 International Conference on 3DTV-Conference.IEEE,2013:350-357.
[52] WANG H,WANG C,LUO H,et al.Object detection in terrestrial laser scanning point clouds based on Hough forest[J].IEEE Geoscience and Remote Sensing Letters,2014,11(10):1807-1811.
[53] WANG Z,ZHANG L,FANG T,et al.A multiscale and hierarchical feature extraction method for terrestrial laser scanning point cloud classification[J].IEEE Transactions on Geoscience and Remote Sensing,2015,53(5):2409-2425.
[54] KIM Y M,MITRA N,YAN D,et al.Acquisition of 3D Indoor Environments with Variability and Repetition[J].ACM Tran-sactions on Graphics,2012,31(6):138.
[55] NAN L,XIE K,SHARf A.A search-classify approach for cluttered indoor scene understanding[J].ACM Transactions on Graphics (TOG),2012,31(6):137.
[56] ZHUANG Y,LU X B,LI Y H,et al.Mobile Robot Indoor Scene Cognition Using 3D Laser Scanning[J].Acta Automatica Sinica,2011,37(10):1232-1240.(in Chinese) 庄严,卢希彬,李云辉,等.移动机器人基于三维激光测距的室内场景认知[J].自动化学报,2011,37(10):1232-1240.

No related articles found!
Full text



[1] LEI Li-hui and WANG Jing. Parallelization of LTL Model Checking Based on Possibility Measure[J]. Computer Science, 2018, 45(4): 71 -75, 88 .
[2] XIA Qing-xun and ZHUANG Yi. Remote Attestation Mechanism Based on Locality Principle[J]. Computer Science, 2018, 45(4): 148 -151, 162 .
[3] LI Bai-shen, LI Ling-zhi, SUN Yong and ZHU Yan-qin. Intranet Defense Algorithm Based on Pseudo Boosting Decision Tree[J]. Computer Science, 2018, 45(4): 157 -162 .
[4] WANG Huan, ZHANG Yun-feng and ZHANG Yan. Rapid Decision Method for Repairing Sequence Based on CFDs[J]. Computer Science, 2018, 45(3): 311 -316 .
[5] SUN Qi, JIN Yan, HE Kun and XU Ling-xuan. Hybrid Evolutionary Algorithm for Solving Mixed Capacitated General Routing Problem[J]. Computer Science, 2018, 45(4): 76 -82 .
[6] ZHANG Jia-nan and XIAO Ming-yu. Approximation Algorithm for Weighted Mixed Domination Problem[J]. Computer Science, 2018, 45(4): 83 -88 .
[7] WU Jian-hui, HUANG Zhong-xiang, LI Wu, WU Jian-hui, PENG Xin and ZHANG Sheng. Robustness Optimization of Sequence Decision in Urban Road Construction[J]. Computer Science, 2018, 45(4): 89 -93 .
[8] LIU Qin. Study on Data Quality Based on Constraint in Computer Forensics[J]. Computer Science, 2018, 45(4): 169 -172 .
[9] ZHONG Fei and YANG Bin. License Plate Detection Based on Principal Component Analysis Network[J]. Computer Science, 2018, 45(3): 268 -273 .
[10] SHI Wen-jun, WU Ji-gang and LUO Yu-chun. Fast and Efficient Scheduling Algorithms for Mobile Cloud Offloading[J]. Computer Science, 2018, 45(4): 94 -99, 116 .