{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,1,11]],"date-time":"2026-01-11T01:13:48Z","timestamp":1768094028170,"version":"3.49.0"},"reference-count":48,"publisher":"MDPI AG","issue":"7","license":[{"start":{"date-parts":[[2023,3,31]],"date-time":"2023-03-31T00:00:00Z","timestamp":1680220800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>Despite the fact that point cloud registration under noisy conditions has recently begun to be tackled by several non-correspondence algorithms, they neither struggle to fuse the global features nor abandon early state estimation during the iterative alignment. To solve the problem, we propose a novel method named R-PCR (recurrent point cloud registration). R-PCR employs a lightweight cross-concatenation module and large receptive network to improve global feature performance. More importantly, it treats the point registration procedure as a high-order Markov decision process and introduces a recurrent neural network for end-to-end optimization. The experiments on indoor and outdoor benchmarks show that R-PCR outperforms state-of-the-art counterparts. The mean average error of rotation and translation of the aligned point cloud pairs are, respectively, reduced by 75% and 66% on the indoor benchmark (ScanObjectNN), and simultaneously by 50% and 37.5% on the outdoor benchmark (AirLoc).<\/jats:p>","DOI":"10.3390\/rs15071889","type":"journal-article","created":{"date-parts":[[2023,4,3]],"date-time":"2023-04-03T01:37:05Z","timestamp":1680485825000},"page":"1889","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":3,"title":["R-PCR: Recurrent Point Cloud Registration Using High-Order Markov Decision"],"prefix":"10.3390","volume":"15","author":[{"given":"Xiaoya","family":"Cheng","sequence":"first","affiliation":[{"name":"College of Systems Engineering, National University of Defense Technology, Changsha 410000, China"}]},{"given":"Shen","family":"Yan","sequence":"additional","affiliation":[{"name":"College of Systems Engineering, National University of Defense Technology, Changsha 410000, China"}]},{"given":"Yan","family":"Liu","sequence":"additional","affiliation":[{"name":"College of Systems Engineering, National University of Defense Technology, Changsha 410000, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-6748-0545","authenticated-orcid":false,"given":"Maojun","family":"Zhang","sequence":"additional","affiliation":[{"name":"College of Systems Engineering, National University of Defense Technology, Changsha 410000, China"}]},{"given":"Chen","family":"Chen","sequence":"additional","affiliation":[{"name":"College of Systems Engineering, National University of Defense Technology, Changsha 410000, China"}]}],"member":"1968","published-online":{"date-parts":[[2023,3,31]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"722","DOI":"10.1109\/LRA.2021.3133593","article-title":"EgoNN: Egocentric Neural Network for Point Cloud Based 6DoF Relocalization at the City Scale","volume":"7","author":"Komorowski","year":"2021","journal-title":"IEEE Robot. Autom. Lett."},{"key":"ref_2","doi-asserted-by":"crossref","unstructured":"Mahmood, B., Han, S., and Lee, D.E. (2020). BIM-based registration and localization of 3D point clouds of indoor scenes using geometric features for augmented reality. Remote. Sens., 12.","DOI":"10.3390\/rs12142302"},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"11","DOI":"10.1016\/j.mechatronics.2015.10.014","article-title":"3D reconstruction and multiple point cloud registration using a low precision RGB-D sensor","volume":"35","author":"Takimoto","year":"2016","journal-title":"Mechatronics"},{"key":"ref_4","doi-asserted-by":"crossref","unstructured":"Bai, X., Luo, Z., Zhou, L., Fu, H., Quan, L., and Tai, C.L. (2020, January 13\u201319). D3Feat: Joint Learning of Dense Detection and Description of 3D Local Features. Proceedings of the 2020 IEEE\/CVF Conference on Computer Vision and Pattern Recognition (CVPR), Seattle, WA, USA.","DOI":"10.1109\/CVPR42600.2020.00639"},{"key":"ref_5","doi-asserted-by":"crossref","unstructured":"Ao, S., Hu, Q., Yang, B., Markham, A., and Guo, Y. (2021, January 20\u201325). Spinnet: Learning a general surface descriptor for 3d point cloud registration. Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, Nashville, TN, USA.","DOI":"10.1109\/CVPR46437.2021.01158"},{"key":"ref_6","doi-asserted-by":"crossref","unstructured":"Huang, S., Gojcic, Z., Usvyatsov, M., Wieser, A., and Schindler, K. (2020). PREDATOR: Registration of 3D Point Clouds with Low Overlap. Comput. Vis. Pattern Recognit., 4267\u20134276.","DOI":"10.1109\/CVPR46437.2021.00425"},{"key":"ref_7","unstructured":"Choy, C., Park, J., and Koltun, V. (November, January 27). Fully convolutional geometric features. Proceedings of the IEEE\/CVF International Conference on Computer Vision, Seoul, Korea."},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"1623","DOI":"10.1007\/s00521-021-06464-y","article-title":"Multi-features guidance network for partial-to-partial point cloud registration","volume":"34","author":"Wang","year":"2022","journal-title":"Neural Comput. Appl."},{"key":"ref_9","doi-asserted-by":"crossref","unstructured":"Qin, Z., Yu, H., Wang, C., Guo, Y., Peng, Y., and Xu, K. (2022, January 18\u201324). Geometric transformer for fast and robust point cloud registration. Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, New Orleans, LA, USA.","DOI":"10.1109\/CVPR52688.2022.01086"},{"key":"ref_10","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_11","doi-asserted-by":"crossref","unstructured":"Yew, Z.J., and Lee, G.H. (2020, January 13\u201319). RPM-Net: Robust Point Matching Using Learned Features. Proceedings of the 2020 IEEE\/CVF Conference on Computer Vision and Pattern Recognition (CVPR), Seattle, WA, USA.","DOI":"10.1109\/CVPR42600.2020.01184"},{"key":"ref_12","unstructured":"Sarode, V., Li, X., Goforth, H., Aoki, Y., Srivatsan, R.A., Lucey, S., and Choset, H. (2019). Pcrnet: Point cloud registration network using pointnet encoding. arXiv."},{"key":"ref_13","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 2019 IEEE\/CVF Conference on Computer Vision and Pattern Recognition (CVPR), Long Beach, CA, USA.","DOI":"10.1109\/CVPR.2019.00733"},{"key":"ref_14","doi-asserted-by":"crossref","unstructured":"Xu, H., Liu, S., Wang, G., Liu, G., and Zeng, B. (2021, January 10\u201317). OMNet: Learning Overlapping Mask for Partial-to-Partial Point Cloud Registration. Proceedings of the IEEE\/CVF International Conference on Computer Vision (ICCV), Montreal, BC, Canada.","DOI":"10.1109\/ICCV48922.2021.00312"},{"key":"ref_15","unstructured":"Sarode, V., Li, X., Goforth, H., Aoki, Y., Dhagat, A., Srivatsan, R.A., Lucey, S., and Choset, H. (2019). One Framework to Register Them All: PointNet Encoding for Point Cloud Alignment. arXiv Comput. Vis. Pattern Recognit."},{"key":"ref_16","unstructured":"Li, X., Pontes, J.K., and Lucey, S. (2020). Deterministic PointNetLK for Generalized Registration. arXiv."},{"key":"ref_17","unstructured":"Yuan, W., Held, D., Mertz, C., and Hebert, M. (2018). Iterative Transformer Network for 3D Point Cloud. arXiv Comput. Vis. Pattern Recognit."},{"key":"ref_18","doi-asserted-by":"crossref","unstructured":"Bauer, D., Patten, T., and Vincze, M. (2021, January 20\u201325). Reagent: Point cloud registration using imitation and reinforcement learning. Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, Nashville, TN, USA.","DOI":"10.1109\/CVPR46437.2021.01435"},{"key":"ref_19","doi-asserted-by":"crossref","unstructured":"Charles, R.Q., Su, H., Kaichun, M., and Guibas, L.J. (2017, January 21\u201326). PointNet: Deep Learning on Point Sets for 3D Classification and Segmentation. Proceedings of the 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Honolulu, HI, USA.","DOI":"10.1109\/CVPR.2017.16"},{"key":"ref_20","doi-asserted-by":"crossref","unstructured":"Cho, K., Van Merri\u00ebnboer, B., Bahdanau, D., and Bengio, Y. (2014). On the properties of neural machine translation: Encoder-decoder approaches. arXiv.","DOI":"10.3115\/v1\/W14-4012"},{"key":"ref_21","first-page":"586","article-title":"Method for registration of 3-D shapes","volume":"Volume 1611","author":"Besl","year":"1992","journal-title":"Proceedings of the Sensor Fusion IV: Control Paradigms and Data Structures"},{"key":"ref_22","doi-asserted-by":"crossref","unstructured":"Censi, A. An ICP variant using a point-to-line metric. Int. Conf. Robot. Autom., 2008.","DOI":"10.1109\/ROBOT.2008.4543181"},{"key":"ref_23","doi-asserted-by":"crossref","unstructured":"Forstner, W., and Khoshelham, K. (2017, January 22\u201329). Efficient and accurate registration of point clouds with plane to plane correspondences. Proceedings of the IEEE International Conference on Computer Vision Workshops, Venice, Italy.","DOI":"10.1109\/ICCVW.2017.253"},{"key":"ref_24","doi-asserted-by":"crossref","unstructured":"Iglesias, J.P., Olsson, C., and Kahl, F. (2020, January 13\u201319). Global optimality for point set registration using semidefinite programming. Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, Seattle, WA, USA.","DOI":"10.1109\/CVPR42600.2020.00831"},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"361","DOI":"10.1016\/j.neucom.2020.02.076","article-title":"Precise iterative closest point algorithm for RGB-D data registration with noise and outliers","volume":"399","author":"Liang","year":"2020","journal-title":"Neurocomputing"},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"73","DOI":"10.1007\/s11263-012-0576-x","article-title":"A Theory of Minimal 3D Point to 3D Plane Registration and Its Generalization","volume":"102","author":"Ramalingam","year":"2013","journal-title":"Int. J. Comput. Vis."},{"key":"ref_27","doi-asserted-by":"crossref","unstructured":"Gojcic, Z., Zhou, C., Wegner, J.D., and Wieser, A. (2019, January 15\u201320). The Perfect Match: 3D Point Cloud Matching With Smoothed Densities. Proceedings of the 2019 IEEE\/CVF Conference on Computer Vision and Pattern Recognition (CVPR), Long Beach, CA, USA.","DOI":"10.1109\/CVPR.2019.00569"},{"key":"ref_28","doi-asserted-by":"crossref","unstructured":"Deng, H., Birdal, T., and Ilic, S. (2018, January 18\u201323). PPFNet: Global Context Aware Local Features for Robust 3D Point Matching. Proceedings of the 2018 IEEE\/CVF Conference on Computer Vision and Pattern Recognition, Salt Lake City, UT, USA.","DOI":"10.1109\/CVPR.2018.00028"},{"key":"ref_29","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 IJCAI\u201981: 7th International Joint Conference on Artificial Intelligence, Vancouver, BC, Canada."},{"key":"ref_30","doi-asserted-by":"crossref","unstructured":"Huang, X., Mei, G., and Zhang, J. (2020, January 13\u201319). Feature-metric Registration: A Fast Semi-supervised Approach for Robust Point Cloud Registration without Correspondences. Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, Seattle, WA, USA.","DOI":"10.1109\/CVPR42600.2020.01138"},{"key":"ref_31","unstructured":"Zhu, M., Ghaffari, M., and Peng, H. (2021). Correspondence-Free Point Cloud Registration with SO(3)-Equivariant Implicit Shape Representations. arXiv Comput. Vis. Pattern Recognit."},{"key":"ref_32","doi-asserted-by":"crossref","unstructured":"Luo, S., Mou, W., Althoefer, K., and Liu, H. (2016, January 9\u201314). Iterative closest labeled point for tactile object shape recognition. Proceedings of the 2016 IEEE\/RSJ International Conference on Intelligent Robots and Systems (IROS), Daejeon, Korea.","DOI":"10.1109\/IROS.2016.7759485"},{"key":"ref_33","doi-asserted-by":"crossref","unstructured":"Peters, M.E., Ammar, W., Bhagavatula, C., and Power, R. (2017). Semi-supervised sequence tagging with bidirectional language models. arXiv.","DOI":"10.18653\/v1\/P17-1161"},{"key":"ref_34","doi-asserted-by":"crossref","unstructured":"Subakan, C., Ravanelli, M., Cornell, S., Bronzi, M., and Zhong, J. (2021, January 6\u201311). Attention is all you need in speech separation. Proceedings of the ICASSP 2021\u20132021 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), Toronto, OT, Canada.","DOI":"10.1109\/ICASSP39728.2021.9413901"},{"key":"ref_35","unstructured":"Fan, H., Zhu, L., and Yang, Y. (February, January 27). Cubic LSTMs for video prediction. Proceedings of the AAAI Conference on Artificial Intelligence, Honolulu, HI, USA."},{"key":"ref_36","unstructured":"Zhou, H., Zhang, S., Peng, J., Zhang, S., Li, J., Xiong, H., and Zhang, W. (March, January 22). Informer: Beyond efficient transformer for long sequence time-series forecasting. Proceedings of the AAAI Conference on Artificial Intelligence, Virtual."},{"key":"ref_37","doi-asserted-by":"crossref","unstructured":"Li, M., Chen, S., Chen, X., Zhang, Y., Wang, Y., and Tian, Q. (2019, January 16\u201317). Actional-structural graph convolutional networks for skeleton-based action recognition. Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, Long Beach, CA, USA.","DOI":"10.1109\/CVPR.2019.00371"},{"key":"ref_38","doi-asserted-by":"crossref","first-page":"1315","DOI":"10.1038\/s41592-019-0598-1","article-title":"Unified rational protein engineering with sequence-based deep representation learning","volume":"16","author":"Alley","year":"2019","journal-title":"Nat. Methods"},{"key":"ref_39","doi-asserted-by":"crossref","unstructured":"Teed, Z., and Deng, J. (2020, January 23\u201328). Raft: Recurrent all-pairs field transforms for optical flow. Proceedings of the European Conference on Computer Vision, Glasgow, UK.","DOI":"10.24963\/ijcai.2021\/662"},{"key":"ref_40","unstructured":"Villegas, R., Yang, J., Zou, Y., Sohn, S., Lin, X., and Lee, H. (2017, January 6\u201311). Learning to generate long-term future via hierarchical prediction. Proceedings of the International Conference on Machine Learning, Sydney, Australia."},{"key":"ref_41","doi-asserted-by":"crossref","first-page":"1735","DOI":"10.1162\/neco.1997.9.8.1735","article-title":"Long short-term memory","volume":"9","author":"Hochreiter","year":"1997","journal-title":"Neural Comput."},{"key":"ref_42","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_43","doi-asserted-by":"crossref","unstructured":"Uy, M.A., Pham, Q.H., Hua, B.S., Nguyen, T., and Yeung, S.K. (2019). Revisiting Point Cloud Classification: A New Benchmark Dataset and Classification Model on Real-World Data. Int. Conf. Comput. Vis.","DOI":"10.1109\/ICCV.2019.00167"},{"key":"ref_44","doi-asserted-by":"crossref","unstructured":"Yan, S., Cheng, X., Liu, Y., Zhu, J., Wu, R., Liu, Y., and Zhang, M. (2023). Render-and-Compare: Cross-View 6 DoF Localization from Noisy Prior. arXiv.","DOI":"10.1109\/ICME55011.2023.00371"},{"key":"ref_45","unstructured":"Loshchilov, I., and Hutter, F. (2017). Fixing Weight Decay Regularization in Adam. CoRR, abs\/1711.05101."},{"key":"ref_46","doi-asserted-by":"crossref","unstructured":"Zhou, Q.Y., Park, J., and Koltun, V. (2016, January 11\u201314). Fast global registration. Proceedings of the European Conference on Computer Vision, Amsterdam, The Netherlands.","DOI":"10.1007\/978-3-319-46475-6_47"},{"key":"ref_47","doi-asserted-by":"crossref","unstructured":"Wang, Y., and Solomon, J. (November, January 27). Deep Closest Point: Learning Representations for Point Cloud Registration. Proceedings of the 2019 IEEE\/CVF International Conference on Computer Vision (ICCV), Seoul, Korea.","DOI":"10.1109\/ICCV.2019.00362"},{"key":"ref_48","doi-asserted-by":"crossref","unstructured":"Hinterstoisser, S., Lepetit, V., Ilic, S., Holzer, S., Bradski, G., Konolige, K., and Navab, N. (2012, January 5\u20139). Model based training, detection and pose estimation of texture-less 3d objects in heavily cluttered scenes. Proceedings of the Asian Conference on Computer Vision, Daejeon, Korea.","DOI":"10.1007\/978-3-642-33885-4_60"}],"container-title":["Remote Sensing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2072-4292\/15\/7\/1889\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,10]],"date-time":"2025-10-10T19:07:55Z","timestamp":1760123275000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2072-4292\/15\/7\/1889"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023,3,31]]},"references-count":48,"journal-issue":{"issue":"7","published-online":{"date-parts":[[2023,4]]}},"alternative-id":["rs15071889"],"URL":"https:\/\/doi.org\/10.3390\/rs15071889","relation":{},"ISSN":["2072-4292"],"issn-type":[{"value":"2072-4292","type":"electronic"}],"subject":[],"published":{"date-parts":[[2023,3,31]]}}}