计算机科学 ›› 2020, Vol. 47 ›› Issue (12): 252-257.doi: 10.11896/jsjkx.191000069
史文凯, 张昭晨, 喻孟娟, 吴瑞, 聂建辉
SHI Wen-kai, ZHANG Zhao-chen, YU Meng-juan, WU Rui, NIE Jian-hui
摘要： 点云对齐是点云数据处理的重要步骤之一粗对齐则是其中的难点.近年来基于深度学习的点云对齐取得了较大进展特别是3DMatch方法能够在噪声、低分辨率以及数据缺失的条件下取得较好的对齐效果.3DMatch采用随机采样的方式产生待匹配点当采样点个数较少时会导致匹配率较低因此对齐效果不佳.为此利用ISS特征点检测代替随机采样然后以3DMatch为特征点生成描述符最后通过匹配特征描述符实现数据对齐.由于ISS特征点检测具有良好的重复性同时3DMatch能够提供具有高区分度的描述符因此该方法大大提高了匹配的鲁棒性和准确性.实验结果表明与随机采样相比特征点采样在初始点云无噪、弱噪和强噪的情况下对齐效果更好、鲁棒性更强并且在粗对齐效果相似的情况下所需特征采样点的个数仅为随机采样点个数的10%极大地提高了对齐的效率.
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| [1] BESL P J.A method for registration 3D shapes[J].IEEE Tran-sactions on Pattern Analysis and Machine Intelligence,1992,14(2):193-200. [2] ZHOU F Q,ZHOU M Q.Improved probability iterative closest point registration agorithm[J].Journal of Graphics,2017,38(1):15-22. [3] ZENG A,SONG S,NIEβNER M,et al.3DMatch:Learning Local Geometric Descriptors from RGB-D Reconstructions[C]//IEEE Conference on Computer Vision and Pattern Recognition.IEEE Computer Society,2017:199-208. [4] LI J,YANG X R,HE B.Geometric Features Matching with Deep Learning[J].Computer Science,2019,46(7):274-279. [5] VALENTIN J,DAI A,NIEβNER M,et al.Learning to Navigate the Energy Landspace[C]//IEEE 2016 Fourth International Conference on 3D Vision(3DV).Stanford,USA,2016:323-332. [6] ZHONG Y.Intrinsic Shape Signatures:A Shape Descriptor for 3d Object Recognition[C]//Proceedings of 2009 IEEE 12th International Conference on Computer Vision Workshops.2009:689-696. [7] AIGER D,MITRA N J,COHEN-OR D.4-points congruent sets for robust pairwise surface registration[J].Acm Transactions on Graphics,2008,27(3):1-10. [8] YU W L,ZHOU M Q,SHUI W Y,et al.AutomaticRegistration Method Based on Curvature[J].Journal of System Simulation,2015,27(10):2374- 2379,2386. [9] LEBEDA K,MATAS J,CHUM O.Fixing Locally OptimizedRANSAC Full Experimental Evaluation[C]//British Machine Vision Conference on Neural Information Processing Systems.2012:1-11. [10] MELLADO N,AIGER D,MITRA N J.Super 4pcs fast global pointcloud registration via smart indexing[J].ComputerGra-phics Forum,2014,3(5):205-215. [11] LU J,PENG Z T,DONGD L.The Registration Algorithm of Point Cloud Based on Optimal Extraction of FPFH Feature[J].New Industrialization Straregy,2014,4(7):75-81. [12] RUSU R B,NICO B,MICHAL B.Fast Point Feature Histo-grams (FPFH) for 3D Registration[C]//2009 IEEE International Conference on Robotics and Automation.Kobe,IEEE,2009:3212-3217. [13] WU Z,SONG S,KHOSLA A,et al.3D ShapeNets:A Deep Re-presentation for Volumetric Shapes[C]//Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition(CVPR).2015:1912-1920. [14] GUO K,ZOU D,CHEN X.3D Mesh Labeling via Deep Convolutional Neural Networks[J].Acm Transactions on Graphics,2015,35(1):1-12. [15] QUAN S W,MA J.On Shortened Local Binary Descriptors[J].Information Science,2020,510:33-49. [16] SRIVASTAVA S,LALL B.DeepPoint3D:Learning Discriminative Local Descriptors using Deep Metric Learning on 3D Point Clouds[J].Pattern Recognition Letters,2019,127:27-36. [17] LOWE D G.Distinctive image features from scale-invari-ant key points[J].International Journal of Computer Vision,2004,60(2):91-110. [18] STEDER B,RUSU R B,KONOLIGE K,et al.Point Feature Extraction On 3D Range Scans Taking Into Account Object Boundaries[C]//Proceedings of 2011 IEEE International Conference on Robotics and Automation.2011:2601-2608. [19] HARRIS C G,STEPHENS M.A combined corner and edge detector[C]//Proceedings of Fourth the Alvey Vision Conference.1998:147-158. [20] ANGELO L D,GIACCARI L.An efficient algorithm for the nearest neighbourhood search for point clouds[J].International Journal of Computer Science Issues,2011,8(5):1-11. |
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