{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,15]],"date-time":"2026-06-15T14:45:48Z","timestamp":1781534748624,"version":"3.54.5"},"reference-count":39,"publisher":"MDPI AG","issue":"5","license":[{"start":{"date-parts":[[2026,5,11]],"date-time":"2026-05-11T00:00:00Z","timestamp":1778457600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100004480","name":"Natural Science Foundation of Shanxi Province","doi-asserted-by":"crossref","award":["202203021212138"],"award-info":[{"award-number":["202203021212138"]}],"id":[{"id":"10.13039\/501100004480","id-type":"DOI","asserted-by":"crossref"}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"crossref","award":["62272426"],"award-info":[{"award-number":["62272426"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"crossref"}]},{"award":["62272426"],"award-info":[{"award-number":["62272426"]}],"id":[{"id":"https:\/\/ror.org\/01h0zpd94","id-type":"ROR","asserted-by":"publisher"}]},{"name":"Foundation of Shanxi Key Laboratory of Machine Vision and Virtual Reality","award":["447-110103"],"award-info":[{"award-number":["447-110103"]}]},{"name":"Shanxi Province Science and Technology Major Special Plan \u201cReveal the List\u201d project","award":["202201150401021"],"award-info":[{"award-number":["202201150401021"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Algorithms"],"abstract":"<jats:p>High-quality correspondences are critical to the accuracy and robustness of point cloud registration. Existing Transformer-based methods are fundamentally constrained by the quadratic computational complexity of self-attention, resulting in limited scalability. Moreover, conventional outlier removal paradigms operate by pruning initial correspondences, and thus fail catastrophically in low-overlap scenarios where initial inliers are inherently scarce. To address these challenges, we propose MaLCA, a point cloud registration method based on Mamba-enhanced features and local correspondence augmentation. We first adopt KPFCN as the backbone to extract multi-scale geometric features from raw point clouds. A Mamba selective state space model then replaces self-attention for global context modeling with linear complexity, while cross-attention is retained to facilitate inter-point-cloud feature interaction. Rather than following the conventional subtraction-based outlier removal paradigm, we introduce a prior-guided local rematching strategy combined with a fused neighbor matching mechanism that iteratively constructs dense, high-quality correspondences from sparse initial inliers, fundamentally overcoming the bottleneck of inlier scarcity in challenging scenes. Extensive experiments on the 3DMatch\/3DLoMatch and 4DMatch\/4DLoMatch benchmarks demonstrate that MaLCA achieves competitive registration performance across both rigid and deformable scenarios, with particular advantages in low-overlap cases.<\/jats:p>","DOI":"10.3390\/a19050380","type":"journal-article","created":{"date-parts":[[2026,5,11]],"date-time":"2026-05-11T17:17:42Z","timestamp":1778519862000},"page":"380","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["MaLCA: Point Cloud Registration with Mamba-Enhanced Features and Local Correspondence Augmentation"],"prefix":"10.3390","volume":"19","author":[{"ORCID":"https:\/\/orcid.org\/0009-0008-3316-3099","authenticated-orcid":false,"given":"Yuchen","family":"Huo","sequence":"first","affiliation":[{"name":"Shanxi Key Laboratory of Machine Vision and Virtual Reality, North University of China, Taiyuan 030051, China"},{"name":"School of Computer Science and Technology, North University of China, Taiyuan 030051, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Longyun","family":"Zhang","sequence":"additional","affiliation":[{"name":"Shanxi Key Laboratory of Machine Vision and Virtual Reality, North University of China, Taiyuan 030051, China"},{"name":"School of Computer Science and Technology, North University of China, Taiyuan 030051, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Huijuan","family":"Guo","sequence":"additional","affiliation":[{"name":"Shanxi Key Laboratory of Machine Vision and Virtual Reality, North University of China, Taiyuan 030051, China"},{"name":"School of Computer Science and Technology, North University of China, Taiyuan 030051, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jingyi","family":"Gong","sequence":"additional","affiliation":[{"name":"Shanxi Key Laboratory of Machine Vision and Virtual Reality, North University of China, Taiyuan 030051, China"},{"name":"School of Computer Science and Technology, North University of China, Taiyuan 030051, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Liqun","family":"Kuang","sequence":"additional","affiliation":[{"name":"Shanxi Key Laboratory of Machine Vision and Virtual Reality, North University of China, Taiyuan 030051, China"},{"name":"School of Computer Science and Technology, North University of China, Taiyuan 030051, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Xie","family":"Han","sequence":"additional","affiliation":[{"name":"Shanxi Key Laboratory of Machine Vision and Virtual Reality, North University of China, Taiyuan 030051, China"},{"name":"School of Computer Science and Technology, North University of China, Taiyuan 030051, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-3596-6457","authenticated-orcid":false,"given":"Fengguang","family":"Xiong","sequence":"additional","affiliation":[{"name":"Shanxi Key Laboratory of Machine Vision and Virtual Reality, North University of China, Taiyuan 030051, China"},{"name":"School of Computer Science and Technology, North University of China, Taiyuan 030051, China"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2026,5,11]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"1405","DOI":"10.1109\/TRO.2026.3666139","article-title":"How Nerfs and 3D Gaussian Splatting Are Reshaping Slam: A Survey","volume":"42","author":"Tosi","year":"2026","journal-title":"IEEE Trans. Robot."},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"634","DOI":"10.1002\/mar.22143","article-title":"Augmented Reality Experiences: Consumer-centered Augmented Reality Framework and Research Agenda","volume":"42","author":"Barta","year":"2025","journal-title":"Psychol. Mark."},{"key":"ref_3","doi-asserted-by":"crossref","unstructured":"Gao, Y., Piccinini, M., Zhang, Y., Wang, D., Moller, K., Brusnicki, R., Zarrouki, B., Gambi, A., Totz, J.F., and Storms, K. (2026). Foundation Models in Autonomous Driving: A Survey on Scenario Generation and Scenario Analysis. IEEE Open J. Intell. Transp. Syst.","DOI":"10.1109\/OJITS.2026.3660686"},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"83","DOI":"10.1007\/s11263-026-02740-3","article-title":"Periodic Vibration Gaussian: Dynamic Urban Scene Reconstruction and Real-Time Rendering","volume":"134","author":"Chen","year":"2026","journal-title":"Int. J. Comput. Vis."},{"key":"ref_5","first-page":"5998","article-title":"Attention Is All You Need","volume":"30","author":"Vaswani","year":"2017","journal-title":"Adv. Neural Inf. Process. Syst."},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"3424","DOI":"10.1109\/TCSI.2025.3649880","article-title":"S2 Mamba: An Efficient Mamba Accelerator with Word-Importance SSM Sparsity","volume":"73","author":"Sun","year":"2026","journal-title":"IEEE Trans. Circuits Syst. I Regul. Pap."},{"key":"ref_7","unstructured":"Gu, A., and Dao, T. (2024, January 7\u20139). Mamba: Linear-Time Sequence Modeling with Selective State Spaces. Proceedings of the First Conference on Language Modeling, Philadelphia, PA, USA."},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"71034","DOI":"10.52202\/079017-2270","article-title":"Metala: Unified Optimal Linear Approximation to Softmax Attention Map","volume":"37","author":"Chou","year":"2024","journal-title":"Adv. Neural Inf. Process. Syst."},{"key":"ref_9","doi-asserted-by":"crossref","unstructured":"Zhang, X., Yang, J., Zhang, S., and Zhang, Y. (2023, January 18\u201322). 3D Registration with Maximal Cliques. Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, Vancouver, BC, Canada.","DOI":"10.1109\/CVPR52729.2023.01702"},{"key":"ref_10","first-page":"32653","article-title":"Pointmamba: A Simple State Space Model for Point Cloud Analysis","volume":"37","author":"Liang","year":"2024","journal-title":"Adv. Neural Inf. Process. Syst."},{"key":"ref_11","doi-asserted-by":"crossref","unstructured":"Rusu, R.B., Blodow, N., and Beetz, M. (2009). Fast Point Feature Histograms (FPFH) for 3D Registration. Proceedings of the 2009 IEEE International Conference on Robotics and Automation, IEEE.","DOI":"10.1109\/ROBOT.2009.5152473"},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"251","DOI":"10.1016\/j.cviu.2014.04.011","article-title":"SHOT: Unique Signatures of Histograms for Surface and Texture Description","volume":"125","author":"Salti","year":"2014","journal-title":"Comput. Vis. Image Underst."},{"key":"ref_13","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, Republic of Korea."},{"key":"ref_14","doi-asserted-by":"crossref","unstructured":"Huang, S., Gojcic, Z., Usvyatsov, M., Wieser, A., and Schindler, K. (2021, January 19\u201325). Predator: Registration of 3d Point Clouds with Low Overlap. Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, Virtual.","DOI":"10.1109\/CVPR46437.2021.00425"},{"key":"ref_15","doi-asserted-by":"crossref","unstructured":"Yu, H., Qin, Z., Hou, J., Saleh, M., Li, D., Busam, B., and Ilic, S. (2023, January 18\u201322). Rotation-Invariant Transformer for Point Cloud Matching. Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, Vancouver, BC, Canada.","DOI":"10.1109\/CVPR52729.2023.00521"},{"key":"ref_16","unstructured":"Thomas, H., Qi, C.R., Deschaud, J.-E., Marcotegui, B., Goulette, F., and Guibas, L.J. (November, January 27). Kpconv: Flexible and Deformable Convolution for Point Clouds. Proceedings of the IEEE\/CVF International Conference on Computer Vision, Seoul, Republic of Korea."},{"key":"ref_17","first-page":"23872","article-title":"Cofinet: Reliable Coarse-to-Fine Correspondences for Robust Pointcloud Registration","volume":"34","author":"Yu","year":"2021","journal-title":"Adv. Neural Inf. Process. Syst."},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"9806","DOI":"10.1109\/TPAMI.2023.3259038","article-title":"Geotransformer: Fast and Robust Point Cloud Registration with Geometric Transformer","volume":"45","author":"Qin","year":"2023","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"ref_19","doi-asserted-by":"crossref","unstructured":"Fu, K., Yuan, M., Wang, C., Pang, W., Chi, J., Wang, M., and Gao, L. (2025, January 11\u201315). Dual Focus-Attention Transformer for Robust Point Cloud Registration. Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, Nashville, TN, USA.","DOI":"10.1109\/CVPR52734.2025.01099"},{"key":"ref_20","unstructured":"Gu, A., Goel, K., and R\u00e9, C. (2022). Efficiently Modeling Long Sequences with Structured State Spaces. arXiv."},{"key":"ref_21","doi-asserted-by":"crossref","unstructured":"Bahri, A., Yazdanpanah, M., Noori, M., Dastani, S., Cheraghalikhani, M., Hakim, G.A.V., Osowiechi, D., Beizaee, F., Ben Ayed, I., and Desrosiers, C. (2025, January 11\u201315). Spectral Informed Mamba for Robust Point Cloud Processing. Proceedings of the Computer Vision and Pattern Recognition Conference, Nashville, TN, USA.","DOI":"10.1109\/CVPR52734.2025.01102"},{"key":"ref_22","doi-asserted-by":"crossref","unstructured":"Han, X., Tang, Y., Wang, Z., and Li, X. (2024). MAMBA3D: Enhancing Local Features for 3D Point Cloud Analysis via State Space Model. Proceedings of the 32nd ACM International Conference on Multimedia, ACM.","DOI":"10.1145\/3664647.3681173"},{"key":"ref_23","unstructured":"Liu, B., Liu, A., Chen, H., Tao, H., Cui, J., Wang, Y., and Zhang, H. (2026). MT-PCR: Hybrid Mamba-Transformer Network with Spatial Serialization for Point Cloud Registration. arXiv."},{"key":"ref_24","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_25","doi-asserted-by":"crossref","first-page":"314","DOI":"10.1109\/TRO.2020.3033695","article-title":"Teaser: Fast and Certifiable Point Cloud Registration","volume":"37","author":"Yang","year":"2020","journal-title":"IEEE Trans. Robot."},{"key":"ref_26","doi-asserted-by":"crossref","unstructured":"Chen, Z., Sun, K., Yang, F., and Tao, W. (2022, January 19\u201324). Sc2-Pcr: A Second Order Spatial Compatibility for Efficient and Robust Point Cloud Registration. Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, New Orleans, LA, USA.","DOI":"10.1109\/CVPR52688.2022.01287"},{"key":"ref_27","doi-asserted-by":"crossref","unstructured":"Bai, X., Luo, Z., Zhou, L., Chen, H., Li, L., Hu, Z., Fu, H., and Tai, C.-L. (2021, January 19\u201325). Pointdsc: Robust Point Cloud Registration Using Deep Spatial Consistency. Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, Virtual.","DOI":"10.1109\/CVPR46437.2021.01560"},{"key":"ref_28","doi-asserted-by":"crossref","unstructured":"Qin, Z., Yu, H., Wang, C., Peng, Y., and Xu, K. (2023, January 18\u201322). Deep Graph-Based Spatial Consistency for Robust Non-Rigid Point Cloud Registration. Proceedings of the Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, Vancouver, BC, Canada.","DOI":"10.1109\/CVPR52729.2023.00522"},{"key":"ref_29","doi-asserted-by":"crossref","unstructured":"Li, Y., and Harada, T. (2022, January 19\u201324). Lepard: Learning Partial Point Cloud Matching in Rigid and Deformable Scenes. Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, New Orleans, LA, USA.","DOI":"10.1109\/CVPR52688.2022.00547"},{"key":"ref_30","doi-asserted-by":"crossref","first-page":"88","DOI":"10.1007\/978-3-030-58558-7_6","article-title":"PointPWC-Net: Cost Volume on Point Clouds for (Self-)Supervised Scene Flow Estimation","volume":"Volume 12350","author":"Vedaldi","year":"2020","journal-title":"Computer Vision\u2014ECCV 2020"},{"key":"ref_31","doi-asserted-by":"crossref","first-page":"527","DOI":"10.1007\/978-3-030-58604-1_32","article-title":"FLOT: Scene Flow on Point Clouds Guided by Optimal Transport","volume":"Volume 12373","author":"Vedaldi","year":"2020","journal-title":"Computer Vision\u2014ECCV 2020"},{"key":"ref_32","unstructured":"Wu, Q., Jiang, H., Luo, L., Li, J., Ding, Y., Xie, J., and Yang, J. (2024). Diff-Reg v1: Diffusion Matching Model for Registration Problem. arXiv."},{"key":"ref_33","doi-asserted-by":"crossref","unstructured":"Donati, N., Sharma, A., and Ovsjanikov, M. (2020, January 14\u201319). Deep Geometric Functional Maps: Robust Feature Learning for Shape Correspondence. Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, Seattle, WA, USA.","DOI":"10.1109\/CVPR42600.2020.00862"},{"key":"ref_34","doi-asserted-by":"crossref","first-page":"2038","DOI":"10.1109\/TPAMI.2022.3164653","article-title":"Multiway Non-Rigid Point Cloud Registration via Learned Functional Map Synchronization","volume":"45","author":"Huang","year":"2022","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"ref_35","doi-asserted-by":"crossref","unstructured":"Newcombe, R.A., Fox, D., and Seitz, S.M. (2015, January 7\u201312). Dynamicfusion: Reconstruction and Tracking of Non-Rigid Scenes in Real-Time. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Boston, MA, USA.","DOI":"10.1109\/CVPR.2015.7298631"},{"key":"ref_36","first-page":"7838","article-title":"Neural Scene Flow Prior","volume":"34","author":"Li","year":"2021","journal-title":"Adv. Neural Inf. Process. Syst."},{"key":"ref_37","doi-asserted-by":"crossref","unstructured":"Park, K., Sinha, U., Barron, J.T., Bouaziz, S., Goldman, D.B., Seitz, S.M., and Martin-Brualla, R. (2021, January 19\u201325). Nerfies: Deformable Neural Radiance Fields. Proceedings of the IEEE\/CVF International Conference on Computer Vision, Virtual.","DOI":"10.1109\/ICCV48922.2021.00581"},{"key":"ref_38","first-page":"27757","article-title":"Non-Rigid Point Cloud Registration with Neural Deformation Pyramid","volume":"35","author":"Li","year":"2022","journal-title":"Adv. Neural Inf. Process. Syst."},{"key":"ref_39","doi-asserted-by":"crossref","unstructured":"Chen, H., Yan, P., Xiang, S., and Tan, Y. (2024, January 17\u201321). Dynamic Cues-Assisted Transformer for Robust Point Cloud Registration. Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, Seattle, WA, USA.","DOI":"10.1109\/CVPR52733.2024.02050"}],"container-title":["Algorithms"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/1999-4893\/19\/5\/380\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,5,13]],"date-time":"2026-05-13T04:21:54Z","timestamp":1778646114000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/1999-4893\/19\/5\/380"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,5,11]]},"references-count":39,"journal-issue":{"issue":"5","published-online":{"date-parts":[[2026,5]]}},"alternative-id":["a19050380"],"URL":"https:\/\/doi.org\/10.3390\/a19050380","relation":{},"ISSN":["1999-4893"],"issn-type":[{"value":"1999-4893","type":"electronic"}],"subject":[],"published":{"date-parts":[[2026,5,11]]}}}