Optics and Precision Engineering, Volume. 32, Issue 11, 1759(2024)

Multi-level filter network for low-overlap point cloud registration

Minqi HE1,2, Li LIU1,2, Shang LI1,2, Hao WU1,2、*, and Dahu ZHU1,2
Author Affiliations
  • 1Hubei Key Laboratory of Advanced Automotive Components Technology, Wuhan University of Technology, Wuhan430070, China
  • 2Hubei Collaborative Innovation Center for Automotive Components Technology, Wuhan University of Technology, Wuhan430070, China
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    References(35)

    [1] [1] 林森, 张强. 应用邻域点信息描述与匹配的点云配准[J]. 光学 精密工程, 2022, 30(8): 984-997. doi: 10.37188/OPE.20223008.0984LINS, ZHANGQ. Point cloud registration using neighborhood point information description and matching[J]. Opt. Precision Eng., 2022, 30(8): 984-997.(in Chinese). doi: 10.37188/OPE.20223008.0984

    [2] YIN M, ZHU Y Y, YIN G F et al. Deep feature interaction network for point cloud registration, with applications to optical measurement of blade profiles[J]. IEEE Transactions on Industrial Informatics, 19, 8614-8624(2023).

    [3] BESL P J, MCKAY N D. A method for registration of 3-D shapes[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 14, 239-256(1992).

    [4] PAVLOV A L, OVCHINNIKOV G W, DERBYSHEV D Y et al. AA-ICP: iterative closest point with Anderson acceleration[C], 21, 3407-3412(2018).

    [5] [5] 余永维, 王康, 杜柳青, 等. 点云模型的匹配点对优化配准[J]. 光学 精密工程, 2023, 31(4): 503-516. doi: 10.37188/ope.20233104.0503YUY W, WANGK, DUL Q, et al. Matching point pair optimization registration method for point cloud model[J]. Opt. Precision Eng., 2023, 31(4): 503-516.(in Chinese). doi: 10.37188/ope.20233104.0503

    [6] [6] 刘跃生, 陈新度, 吴磊, 等. 混合稀疏迭代最近点配准[J]. 光学 精密工程, 2021, 29(9): 2255-2267. doi: 10.37188/OPE.20212909.2255LIUY SH, CHENX D, WUL, et al. Mixed sparse iterative nearest point registration[J]. Opt. Precision Eng., 2021, 29(9): 2255-2267.(in Chinese). doi: 10.37188/OPE.20212909.2255

    [7] ZHANG J Y, YAO Y X, DENG B L. Fast and robust iterative closest point[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 44, 3450-3466(2022).

    [8] LV R, LIU H D, WANG Z J et al. WPMAVM: weighted plus-and-minus allowance variance minimization algorithm for solving matching distortion[J]. Robotics and Computer-Integrated Manufacturing, 76, 102320(2022).

    [9] [9] 吴浩, 冯晓志, 华林, 等. 基于去伪加权方差最小化算法的大型复杂零部件局部配准全局方法研究[J]. 中国科学: 技术科学, 2024, 54(3): 422-442. doi: 10.1360/sst-2023-0013WUH, FENGX ZH, HUAL, et al. Local-to-global registration method of large complex components based on a de-pseudo-weighted variance minimization algorithm[J]. Scientia Sinica (Technologica), 2024, 54(3): 422-442.(in Chinese). doi: 10.1360/sst-2023-0013

    [10] FISCHLER M A, BOLLES R C. Random Sample Consensus A Paradigm for Model Fitting with Applications to Image Analysis and Automated Cartography[M]. Readings in Computer Vision, 726-740(1987).

    [11] YANG Z F, WANG X G, HOU J. A 4PCS coarse registration algorithm based on ISS feature points[C], 26, 7371-7375(2021).

    [12] AIGER D, MITRA N J, COHEN-OR D. 4-points congruent sets for robust pairwise surface registration[J]. ACM Transactions on Graphics, 27, 1-10(2008).

    [13] STECHSCHULTE JM AHMED N, HECKMAN C. Robust low-overlap 3-D point cloud registration for outlier rejection[C], 7143-7149(2019).

    [14] ZHONG B, LI D. SliceLRF: a local reference frame sliced along the height on the 3D surface[J]. Sensors, 23, 3483(2023).

    [15] RUSU R B, BLODOW N, BEETZ M. Fast point feature histograms (FPFH) for 3D registration[C], 1848-1853(2009).

    [16] LIU X S, LI A H, SUN J F et al. Trigonometric projection statistics histograms for 3D local feature representation and shape description[J]. Pattern Recognition, 143, 109727(2023).

    [17] ZHOU Q Y, PARK J, KOLTUN V. Fast Global Registration[M]. Computer Vision-ECCV 2016, 766-782(2016).

    [18] HUANG S Y, GOJCIC Z, USVYATSOV M et al. PREDATOR: Registration of 3D point clouds with low overlap[C], 4265-4274(2021).

    [19] HUANG X S, QU W T, ZUO Y F et al. IMFNet: interpretable multimodal fusion for point cloud registration[J]. IEEE Robotics and Automation Letters, 7, 12323-12330(2022).

    [20] CHOY C, PARK J, KOLTUN V. Fully convolutional geometric features[C], 8957-8965(2019).

    [21] WANG Y, SOLOMON J. Deep closest point: learning representations for point cloud registration[C], 3522-3531(2019).

    [22] QIN Z, YU H, WANG C et al. Geometric transformer for fast and robust point cloud registration[C], 18, 11133-11142(2022).

    [23] YEW Z J, LEE G H. REGTR: end-to-end point cloud correspondences with transformers[C], 18, 6667-6676(2022).

    [24] ZHANG X Y, YANG J Q, ZHANG S K et al. 3D registration with maximal cliques[C], 17, 17745-17754(2023).

    [25] YEW Z J, LEE G H. RPM-net: robust point matching using learned features[C], 13, 11821-11830(2020).

    [26] QIU S, ANWAR S, BARNES N. Semantic segmentation for real point cloud scenes via bilateral augmentation and adaptive fusion[C], 20, 1757-1767(2021).

    [27] CHOY C, GWAK J, SAVARESE S. 4D spatio-temporal ConvNets: minkowski convolutional neural networks[C], 15, 3070-3079(2019).

    [28] LOWE D G. Object recognition from local scale-invariant features[C], 20, 1150-1157(1999).

    [29] SARLIN P E, DETONE D, MALISIEWICZ T et al. SuperGlue: learning feature matching with graph neural networks[C], 13, 4937-4946(2020).

    [30] CUTURI M. Sinkhorn distances: lightspeed computation of optimal transport[C], 2292-2300(2013).

    [31] YU H, LI F, SALEH M et al. CoFiNet: reliable coarse-to-fine correspondences for robust point cloud registration[J]. arXiv. arXiv:.

    [32] LI J X, CHEN B M, LEE G H. SO-Net: Self-organizing network for point cloud analysis[C], 9397-9406(2018).

    [33] SUN Y F, CHENG C M, ZHANG Y H. Circle loss: a unified perspective of pair similarity optimization[C], 6397-6406(2020).

    [34] ZENG A, SONG S R, NIESSNER M et al. 3DMatch: learning local geometric descriptors from RGB-D reconstructions[C], 199-208(2017).

    [35] MELLADO N, AIGER D, MITRA N J. Super 4PCS Fast Global Pointcloud Registration via Smart Indexing[J]. Computer Graphics Forum, 33, 205-215(2014).

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    Minqi HE, Li LIU, Shang LI, Hao WU, Dahu ZHU. Multi-level filter network for low-overlap point cloud registration[J]. Optics and Precision Engineering, 2024, 32(11): 1759

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    Paper Information

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    Received: Nov. 6, 2023

    Accepted: --

    Published Online: Aug. 8, 2024

    The Author Email: Hao WU (wuhao2023@whut.edu.cn)

    DOI:10.37188/OPE.20243211.1759

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