{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,17]],"date-time":"2026-06-17T16:23:46Z","timestamp":1781713426272,"version":"3.54.5"},"reference-count":142,"publisher":"Frontiers Media SA","license":[{"start":{"date-parts":[[2024,1,4]],"date-time":"2024-01-04T00:00:00Z","timestamp":1704326400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":["frontiersin.org"],"crossmark-restriction":true},"short-container-title":["Front. Neurorobot."],"abstract":"<jats:p>With the development of 3D scanning devices, point cloud registration is gradually being applied in various fields. Traditional point cloud registration methods face challenges in noise, low overlap, uneven density, and large data scale, which limits the further application of point cloud registration in actual scenes. With the above deficiency, point cloud registration methods based on deep learning technology gradually emerged. This review summarizes the point cloud registration technology based on deep learning. Firstly, point cloud registration based on deep learning can be categorized into two types: complete overlap point cloud registration and partially overlapping point cloud registration. And the characteristics of the two kinds of methods are classified and summarized in detail. The characteristics of the partially overlapping point cloud registration method are introduced and compared with the completely overlapping method to provide further research insight. Secondly, the review delves into network performance improvement summarizes how to accelerate the point cloud registration method of deep learning from the hardware and software. Then, this review discusses point cloud registration applications in various domains. Finally, this review summarizes and outlooks the current challenges and future research directions of deep learning-based point cloud registration.<\/jats:p>","DOI":"10.3389\/fnbot.2023.1281332","type":"journal-article","created":{"date-parts":[[2024,1,4]],"date-time":"2024-01-04T04:33:39Z","timestamp":1704342819000},"update-policy":"https:\/\/doi.org\/10.3389\/crossmark-policy","source":"Crossref","is-referenced-by-count":34,"title":["A review of rigid point cloud registration based on deep learning"],"prefix":"10.3389","volume":"17","author":[{"given":"Lei","family":"Chen","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Changzhou","family":"Feng","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yunpeng","family":"Ma","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yikai","family":"Zhao","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Chaorong","family":"Wang","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1965","published-online":{"date-parts":[[2024,1,4]]},"reference":[{"key":"B1","doi-asserted-by":"publisher","DOI":"10.1145\/1399504.1360684","article-title":"\u201c4-points congruent sets for robust pairwise surface registration,\u201d","author":"Aiger","year":"2008","journal-title":"ACM SIGGRAPH 2008"},{"key":"B2","doi-asserted-by":"publisher","first-page":"13095","DOI":"10.1109\/CVPR46437.2021.01290","article-title":"\u201cRpsrnet: end-to-end trainable rigid point set registration network using barnes-hut 2d-tree representation,\u201d","author":"Ali","year":"2021","journal-title":"2021 IEEE\/CVF Conference on Computer Vision and Pattern Recognition (CVPR)"},{"key":"B3","doi-asserted-by":"publisher","first-page":"7156","DOI":"10.1109\/CVPR.2019.00733","article-title":"\u201cPointnetlk: robust &efficient point cloud registration using pointnet,\u201d","author":"Aoki","year":"2019","journal-title":"2019 IEEE\/CVF Conference on Computer Vision and Pattern Recognition (CVPR)"},{"key":"B4","author":"Bader","year":"2012","journal-title":"Space-Filling Curves: An Introduction With Applications in Scientific Computing"},{"key":"B5","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR46437.2021.01560","article-title":"\u201cPOINTDSC: robust point cloud registration using deep spatial consistency,\u201d","author":"Bai","year":"2021","journal-title":"Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition"},{"key":"B6","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR42600.2020.00639","article-title":"\u201cD3feat: joint learning of dense detection and description of 3d local features,\u201d","author":"Bai","year":"2020","journal-title":"Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition"},{"key":"B7","doi-asserted-by":"publisher","first-page":"163","DOI":"10.1109\/ISPASS.2009.4919648","article-title":"\u201cAnalyzing cuda workloads using a detailed gpu simulator,\u201d","author":"Bakhoda","year":"2009"},{"key":"B8","doi-asserted-by":"publisher","first-page":"446","DOI":"10.1038\/324446a0","article-title":"A hierarchical o (n log n) force-calculation algorithm","volume":"324","author":"Barnes","year":"1986","journal-title":"Nature"},{"key":"B9","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2019.00446","article-title":"\u201cA general and adaptive robust loss function,\u201d","author":"Barron","year":"2019","journal-title":"Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition"},{"key":"B10","doi-asserted-by":"publisher","first-page":"1729","DOI":"10.3390\/rs12111729","article-title":"Deep learning on 3D point clouds","volume":"12","author":"Bello","year":"2020","journal-title":"Rem. 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