{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,12,29]],"date-time":"2025-12-29T19:01:15Z","timestamp":1767034875191,"version":"build-2065373602"},"reference-count":25,"publisher":"MDPI AG","issue":"21","license":[{"start":{"date-parts":[[2021,10,28]],"date-time":"2021-10-28T00:00:00Z","timestamp":1635379200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["6217010132"],"award-info":[{"award-number":["6217010132"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>Point cloud registration is a key step in the reconstruction of 3D data models. The traditional ICP registration algorithm depends on the initial position of the point cloud. Otherwise, it may get trapped into local optima. In addition, the registration method based on the feature learning of PointNet cannot directly or effectively extract local features. To solve these two problems, this paper proposes SAP-Net, inspired by CorsNet and PointNet++, as an optimized CorsNet. To be more specific, SAP-Net firstly uses the set abstraction layer in PointNet++ as the feature extraction layer and then combines the global features with the initial template point cloud. Finally, PointNet is used as the transform prediction layer to obtain the six parameters required for point cloud registration directly, namely the rotation matrix and the translation vector. Experiments on the ModelNet40 dataset and real data show that SAP-Net not only outperforms ICP and CorsNet on both seen and unseen categories of the point cloud but also has stronger robustness.<\/jats:p>","DOI":"10.3390\/s21217177","type":"journal-article","created":{"date-parts":[[2021,10,28]],"date-time":"2021-10-28T23:52:35Z","timestamp":1635465155000},"page":"7177","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":5,"title":["SAP-Net: A Simple and Robust 3D Point Cloud Registration Network Based on Local Shape Features"],"prefix":"10.3390","volume":"21","author":[{"given":"Jinlong","family":"Li","sequence":"first","affiliation":[{"name":"School of Physical Science and Technology, Southwest Jiaotong University, Chengdu 610031, China"}]},{"given":"Yuntao","family":"Li","sequence":"additional","affiliation":[{"name":"School of Physical Science and Technology, Southwest Jiaotong University, Chengdu 610031, China"}]},{"given":"Jiang","family":"Long","sequence":"additional","affiliation":[{"name":"School of Physical Science and Technology, Southwest Jiaotong University, Chengdu 610031, China"}]},{"given":"Yu","family":"Zhang","sequence":"additional","affiliation":[{"name":"School of Physical Science and Technology, Southwest Jiaotong University, Chengdu 610031, China"}]},{"given":"Xiaorong","family":"Gao","sequence":"additional","affiliation":[{"name":"School of Physical Science and Technology, Southwest Jiaotong University, Chengdu 610031, China"}]}],"member":"1968","published-online":{"date-parts":[[2021,10,28]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1561\/2300000035","article-title":"A review of point cloud registration algorithms for mobile robotics","volume":"4","author":"Pomerleau","year":"2015","journal-title":"Found. Trends Robot."},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"3412","DOI":"10.1109\/TNNLS.2020.3015992","article-title":"Deep learning for lidar point clouds in autonomous driving: A review","volume":"32","author":"Li","year":"2021","journal-title":"IEEE Trans. Neural Netw. Learn. Syst."},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"498","DOI":"10.1109\/TRO.2018.2882730","article-title":"Real-time global registration for globally consistent rgb-d slam","volume":"35","author":"Han","year":"2019","journal-title":"IEEE Trans. Robot."},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"55","DOI":"10.1016\/j.isprsjprs.2015.04.016","article-title":"Accurate and robust registration of high-speed railway viaduct point clouds using closing conditions and external geometric constraints","volume":"106","author":"Ji","year":"2015","journal-title":"ISPRS J. Photogramm. Remote Sens."},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"239","DOI":"10.1109\/34.121791","article-title":"A Method for Registration of 3-D Shapes","volume":"14","author":"Besl","year":"1992","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"ref_6","first-page":"435","article-title":"Generalized-ICP","volume":"2","author":"Segal","year":"2009","journal-title":"Proc. Robot. Sci. Syst."},{"key":"ref_7","first-page":"247","article-title":"Multi-Channel Generalized-ICP: A robust framework for multi-channel scan registration","volume":"87","author":"James","year":"2016","journal-title":"Robot. Auton. Syst."},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"113","DOI":"10.1111\/cgf.12178","article-title":"Sparse Iterative Closest Point","volume":"32","author":"Bouaziz","year":"2013","journal-title":"Comput. Graph. Forum."},{"key":"ref_9","doi-asserted-by":"crossref","unstructured":"Yang, J., Li, H., and Jia, Y. (2013, January 1\u20138). Go-ICP: Solving 3D registration efficiently and globally optimally. Proceedings of the IEEE International Conference on Computer Vision, Sydney, Australia.","DOI":"10.1109\/ICCV.2013.184"},{"key":"ref_10","unstructured":"Biber, P., and Strasser, W. (2003, January 27\u201331). The normal distributions transform: A new approach to laser scan matching. Proceedings of the 2003 IEEE International Conference on Intelligent Robots and Systems, Las Vegas, NV, USA."},{"key":"ref_11","doi-asserted-by":"crossref","unstructured":"Evangelidis, G., Kounades-Bastian, D., Horaud, R., and Psarakis, E.Z. (2014, January 6\u201312). A generative model for the joint registration of multiple point sets. Proceedings of the European Conference on Computer Vision ECCV 2014, Zurich, Switzerland.","DOI":"10.1007\/978-3-319-10584-0_8"},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"2262","DOI":"10.1109\/TPAMI.2010.46","article-title":"Point Set Registration: Coherent Point Drift","volume":"32","author":"Myronenko","year":"2010","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"381","DOI":"10.1145\/358669.358692","article-title":"Random Sample Consensus: A Paradigm for Model Fitting with Applications to Image Analysis and Automated Cartography","volume":"24","author":"Fischler","year":"1981","journal-title":"Commun. ACM"},{"key":"ref_14","unstructured":"Qi, C.R., Su, H., Mo, K., and Guibas, L.J. (2017, January 21\u201326). PointNet: Deep Learning on Point Sets for 3D Classification and Segmentation. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Honolulu, HI, USA."},{"key":"ref_15","unstructured":"Qi, C.R., Yi, L., Su, H., and Guibas, L.J. (2017). PointNet++: Deep Hierarchical Feature Learning on Point Sets in a Metric Space. arXiv."},{"key":"ref_16","first-page":"820","article-title":"PointCNN: Convolution On X-Transformed Points","volume":"31","author":"Li","year":"2018","journal-title":"Adv. Neural Inf. Process. Syst."},{"key":"ref_17","first-page":"1","article-title":"Dynamic Graph CNN for Learning on Point Clouds","volume":"38","author":"Wang","year":"2019","journal-title":"ACM Trans. Graph."},{"key":"ref_18","doi-asserted-by":"crossref","unstructured":"Aoki, Y., Goforth, H., Srivatsan, R.A., and Lucey, S. (2019, January 15\u201320). PointNetLK: Robust & Efficient Point Cloud Registration Using PointNet. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Long Beach, CA, USA.","DOI":"10.1109\/CVPR.2019.00733"},{"key":"ref_19","unstructured":"Lucas, B.D., and Kanade, T. (1981, January 24\u201328). An Iterative Image Registration Technique with an Application to Stereo Vision. Proceedings of the International Joint Conference on Artificial Intelligence (IJCAI), Vancouver, BC, Canada."},{"key":"ref_20","unstructured":"Wang, Y., and Solomon, J. (November, January 27). Deep Closest Point: Learning Representations for Point Cloud Registration. Proceedings of the IEEE International Conference on Computer Vision, Seoul, Korea."},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"240","DOI":"10.1145\/3355089.3356573","article-title":"RPM-Net: Recurrent Prediction of Motion and Parts from Point Cloud","volume":"38","author":"Yan","year":"2019","journal-title":"ACM Trans. Graph."},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"1019","DOI":"10.1016\/S0031-3203(98)80010-1","article-title":"New Algorithms for 2D and 3D Point Matching: Pose Estimation and Correspondence","volume":"31","author":"Gold","year":"1998","journal-title":"Pattern Recognit."},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"3960","DOI":"10.1109\/LRA.2020.2970946","article-title":"CorsNet: 3D Point Cloud Registration by Deep Neural Network","volume":"5","author":"Kurobe","year":"2020","journal-title":"IEEE Robot. Autom. Lett."},{"key":"ref_24","unstructured":"Wu, Z., Song, S., Khosla, A., Yu, F., Zhang, L., Tang, X., and Xiao, J. (2015, January 7\u201312). 3D ShapeNets: A deep representation for volumetric shapes. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Boston, MA, USA."},{"key":"ref_25","unstructured":"Kingma, D.P., and Ba, J. (2015). Adam: A method for stochastic optimization. arXiv."}],"container-title":["Sensors"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/1424-8220\/21\/21\/7177\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T07:22:19Z","timestamp":1760167339000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/1424-8220\/21\/21\/7177"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2021,10,28]]},"references-count":25,"journal-issue":{"issue":"21","published-online":{"date-parts":[[2021,11]]}},"alternative-id":["s21217177"],"URL":"https:\/\/doi.org\/10.3390\/s21217177","relation":{},"ISSN":["1424-8220"],"issn-type":[{"type":"electronic","value":"1424-8220"}],"subject":[],"published":{"date-parts":[[2021,10,28]]}}}