计算机科学 ›› 2024, Vol. 51 ›› Issue (2): 142-150.doi: 10.11896/jsjkx.230200073
刘旭珩, 柏正尧, 许祝, 杜佳锦, 肖霄
LIU Xuheng, BAI Zhengyao, XU Zhu, DU Jiajin, XIAO Xiao
摘要: 针对点云配准过程中仅仅利用点云特征寻求对应关系使得离群点多、配准精度不高的问题进行研究,提出了一种使用点云之间匹配点概率矩阵和点云空间信息特征矩阵共同搜寻对应关系,并且相互配合确定对应点权重的点云配准网络——AMGNet。首先使用点云特征提取网络获得两片待配准点云的高维特征;然后采用Transformer对独立特征进行上下文信息融合,之后利用关键点提取模块选取出特征更强的点,使用SoftBBS方法获得点云匹配点概率矩阵后,结合点云空间特征矩阵搜索到最终的对应关系,同时,权重分配也使用了双重矩阵共同决定的策略;最后使用奇异值分解获得需要的刚性变换矩阵。在ModelNet40,7Scenes等人工合成数据集和真实场景数据集上进行了多次实验。结果表明,在ModelNet40目标未知实验中的旋转矩阵和平移向量的均方误差分别降低至0.025和0.004 6。AMGNet配准精度较高,抗干扰能力强,泛化能力强。
中图分类号:
[1]LI Z M,ZHANG Y P,LIU Y J,et al.Deformable Graph Convolutional Networks Based Point Cloud Representation Learning[J].Computer Science,2022,49(8):273-278. [2]HUANG X,MEI G,ZHANG J,et al.A comprehensive survey on point cloud registration[J].arXiv:2103.02690,2021. [3]QIN H X,LIU Z T,TAN B Y.Review on deep learning rigid point cloud registration[J].Journal of Image and Graphics,2022,27(2):329-348. [4]LI J W,ZHAN J W.Review on 3D point cloud registrationmethod[J].Journal of Image and Graphics,2022,27(2):349-367. [5]LI J,ZHANG C,XU Z,et al.Iterative distance-aware similarity matrix convolution with mutual-supervised point elimination for efficient point cloud registration[C]//European Conference on Computer Vision.Berlin:Springer,2020:378-394. [6]WANG H,LIU X,KANG W,et al.Multi-features guidance net-work for partial-to-partial point cloud registration[J].Neural Computing and Applications,2022,34(2):1623-1634. [7]HEZRONI I,DRORY A,GIRYES R,et al.DeepBBS:Deep Best Buddies for Point Cloud Registration[C]//2021 International Conference on 3DVision(3DV).Piscataway,NJ:IEEE.2021:342-351. [8]WANG Y,SOLOMON J M.Deep closest point:Learning rep resentations for point cloud registration[C]//Proceedings of the IEEE/CVF International Conference on Computer Vision.Piscataway,NJ:IEEE 2019:3523-3532. [9]YEW Z J,LEE G H.RPM-Net:Robust Point Matching usingLearned Features[C]//2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition(CVPR).Piscataway,NJ:IEEE,2020:11821-11830. [10]BESL P J,MCKAY N D.Method for registration of 3-D shapes[C]//Sensor Fusion IV:Control Paradigms and Data Structures.Spie,1992:586-606. [11]YANG J,LI H,CAMPBALL D,et al.Go-ICP:A globally optimal solution to 3D ICP point-set registration[J].IEEE Transactions on Pattern Analysis and Machine Intelligence,2015,38(11):2241-2254. [12]HEXSEL B,VHAVLE H,CHEN Y.DICP:Doppler IterativeClosest Point Algorithm[J].arXiv:2201.11944,2022. [13]MAGNUSSON M,LILIENTHAL A,DUCKETT T.Scan registration for autonomous mining vehicles using 3D-NDT[J].Journal of Field Robotics,2007,24(10):803-827. [14]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. [15]FISCHLER M A,BOLLES R C.Random sample consensus:aparadigm for model fitting with applications to image analysis and automated cartography[J].Communications of the ACM,1981,24(6):381-395. [16]MELLADO N,AIGER D,MITRA N J.Super 4PCS Fast Global Pointcloud Registration via Smart Indexing[J].Computer Graphics Forum,2014,33(5):205-215. [17]KAMOUSI P,LAZARD S,MAHESHWARI A,et al.Analysis of farthest point sampling for approximating geodesics in a graph[J].Computational Geometry,2016,57:1-7. [18] RUSINKIEWICZ S,LEVOV M.Efficient variants of the ICP algorithm[C]//Proceedings third International Conference on 3-D Digital Imaging and Modeling.Piscataway,NJ:IEEE,2001:145-152. [19]PRAKHYA S M,LIU B,LIN W.Detecting keypoint sets on 3D point clouds via Histogram of Normal Orientations[J].Pattern Recognition Letters,2016,83:42-48. [20]CHUA C S.Point Signatures:A New Representation for 3D Object Recognition[J].International Journal of Computer Vision,1997,25:63-85. [21]FROME A,HUBER D,KOLLURI R,et al.Recognizing objects in range data using regional point dscriptors[C]//European Conference on Computer Vision.Berlin:Springer:2004:224-237. [22]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.Piscataway,NJ:IEEE.2008:3384-3391. [23]RUSU R B,BLODOW N,BEETZ M.Fast point feature histo-gra.ms(FPFH) for 3D registration[C]//2009 IEEE International Conference on Robotics and Automation.Piscataway,NJ:IEEE,2009:3212-3217. [24] ZHANG W L,CHENG L,REN M F,et al.Point Cloud Registration Based on AGConv Local Feature Descriptors[J].Computer Engineering,2023,49(11):231-237. [25] LI X M,WANG C Y,LIU Xl,et al.Point cloud registration method based on supervoxel bidirectional nearest neighbor distance ratio[J].Journal of Jilin University(Engineering and Technology Edition),2022,52(8):1918-1925. [26]AOKI Y,GOFORTH H,SRIVATSAN R A,et al.Pointnetlk:Robust & efficient point cloud registration using pointnet[C]//Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition.Piscataway,NJ:IEEE,2019:7163-7172. [27]LU W,WAN G,ZHOU Y,et al.Deepvcp:An end-to-end deep neural network for point cloud registration[C]//Proceedings of the IEEE/CVF International Conference on Computer Vision.Piscataway,NJ:IEEE,2019:12-21. [28]MONTI F,BOSCAINI D,MASCI J,et al.Geometricdeep lear-ning on graphs and manifolds using mixture model cnns[C]//2017 IEEE Conference on Computer Vision and Pattern Recognition(CVPR).Piscataway,NJ:IEEE,2017:5425-5434. [29]CHARLES R Q,SU H,KAICHUN M,et al.PointNet:Deep Learning on Point Sets for 3D Classification and Segmentation[C]//2017 IEEE Conference on Computer Vision and Pattern Recognition(CVPR).Piscataway,NJ:IEEE,2017:77-85. [30]WANG Y,SUN Y,LIU Z,et al.Dynamic graph cnn for learning on point clouds[J].ACM Transactions on Graphics(TOG),2019,38(5):1-12. [31]QI C R,YI L,SU H,et al.Pointnet++:Deep hierarchical feature learning on point sets in a metric space[J].arXiv:1706.02413,2017. [32]VASWANI A,SHAZEER N,PARMAR N,et al.Attention isall you need[J].arXiv:1706.03762,2017. [33]LI J,ZHANG C,XU Z,et al.Iterative distance-aware similarity matrix convolution with mutual-supervised point elimination for efficient point cloud registration[C]//Computer Vision-ECCV 2020:16th European Conference,Part XXIV 16.Springer International Publishing,2020:378-394. [34]WU Z R,SONG S,KHOSLA A,et al.3D ShapeNets:A deep representation for volumetric shapes[C]//2015 IEEE Confe-rence on Computer Vision and Pattern Recognition(CVPR).Piscataway,NJ:IEEE,2015:1912-1920. [35] SHOTTON J,GLOCKER B,ZACH C,et al.Scene coordinate regression forests for camera relocalization in RGB-D images[C]//Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition.Piscataway,NJ:IEEE,2013:2930-2937. [36] ZHOU Q Y,PARK J,KOLTUN V.Open3D:A modern library for 3D data processing[J].arXiv:1801.09847,2018. [37]SHEN Y,HUI L,JIANG H,et al.Reliable Inlier Evaluation for Unsupervised Point Cloud Registration[C]//Proceedings of the AAAI Conference on Artificial Intelligence.California:Association for the Advancement of Artificial Intelligence(AAAI),2022:2198-2206. |
|