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Intell."],"abstract":"<jats:title>Abstract<\/jats:title><jats:p>Correspondence-based six-degree-of-freedom (6-DoF) pose estimation remains a mainstream solution for 3D point cloud registration. However, the heavy outliers pose great challenges to this problem. In this paper, we propose a random sample consensus (RANSAC) variant based on sampling locally and hypothesis globally (SLHG) for 6-DoF pose estimation and 3D point cloud registration. The key novelties are efficient sampling by guiding the sampling process locally and accurate pose estimation by generating hypotheses with global information. SLHG first generates a correspondence subset via compatibility clustering on the initial set. Second, locally guided graph sampling is performed. Third, 6-DoF hypotheses are generated by incorporating global information with a voting scheme. The best hypothesis serves as the estimation result by repeating the second and third steps. Extensive experiments on four popular datasets and comparisons with state-of-the-art methods confirm that: SLHG manages to 1) achieve accurate registrations with a few iterations, and 2) yield better accuracy performance than most competitors.<\/jats:p>","DOI":"10.1007\/s44267-023-00022-x","type":"journal-article","created":{"date-parts":[[2023,9,2]],"date-time":"2023-09-02T00:01:57Z","timestamp":1693612917000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":19,"title":["Sampling locally, hypothesis globally: accurate 3D point cloud registration with a RANSAC variant"],"prefix":"10.1007","volume":"1","author":[{"given":"Yuxin","family":"Cheng","sequence":"first","affiliation":[]},{"given":"Zhiqiang","family":"Huang","sequence":"additional","affiliation":[]},{"given":"Siwen","family":"Quan","sequence":"additional","affiliation":[]},{"given":"Xinyue","family":"Cao","sequence":"additional","affiliation":[]},{"given":"Shikun","family":"Zhang","sequence":"additional","affiliation":[]},{"given":"Jiaqi","family":"Yang","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2023,9,2]]},"reference":[{"key":"22_CR1","doi-asserted-by":"crossref","first-page":"1815","DOI":"10.1109\/TMM.2021.3073265","volume":"24","author":"C. 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Pointnet: deep learning on point sets for 3D classification and segmentation. In Proceedings of the IEEE conference on computer vision and pattern recognition (pp.\u00a07163\u20137172). Los Alamitos: IEEE."},{"key":"22_CR50","doi-asserted-by":"crossref","unstructured":"Qin, Z., Yu, H., Wang, C., Guo, Y., Peng, Y., & Xu, K. (2022). Geometric transformer for fast and robust point cloud registration. arXiv preprint. arXiv:2202.06688.","DOI":"10.1109\/CVPR52688.2022.01086"},{"key":"22_CR51","first-page":"7193","volume-title":"Proceedings of the IEEE conference on computer vision and pattern recognition","author":"G. D. Pais","year":"2020","unstructured":"Pais, G. D., Ramalingam, S., Govindu, V. M., Nascimento, J. C., Chellappa, R., & Miraldo, P. (2020). 3DRegNet: a deep neural network for 3D point registration. In Proceedings of the IEEE conference on computer vision and pattern recognition (pp.\u00a07193\u20137203). 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Eindhoven: Eurographics Association."},{"issue":"3","key":"22_CR54","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1145\/1360612.1360684","volume":"27","author":"D. Aiger","year":"2008","unstructured":"Aiger, D., Mitra, N. J., & Cohen-Or, D. (2008). 4-points congruent sets for robust pairwise surface registration. ACM Transactions on Graphics, 27(3), 1\u201310.","journal-title":"ACM Transactions on Graphics"},{"key":"22_CR55","first-page":"998","volume-title":"Proceedings of the IEEE conference on computer vision and pattern recognition","author":"B. Drost","year":"2010","unstructured":"Drost, B., Ulrich, M., Navab, N., & Ilic, S. (2010). Model globally, match locally: efficient and robust 3D object recognition. In Proceedings of the IEEE conference on computer vision and pattern recognition (pp.\u00a0998\u20131005). Los Alamitos: IEEE."},{"issue":"1","key":"22_CR56","doi-asserted-by":"crossref","first-page":"63","DOI":"10.1007\/s11263-013-0627-y","volume":"105","author":"Y. Guo","year":"2013","unstructured":"Guo, Y., Sohel, F., Bennamoun, M., Lu, M., & Wan, J. (2013). Rotational projection statistics for 3D local surface description and object recognition. International Journal of Computer Vision, 105(1), 63\u201386.","journal-title":"International Journal of Computer Vision"},{"key":"22_CR57","first-page":"4137","volume-title":"Proceedings of the IEEE international conference on computer vision","author":"A. G. Buch","year":"2017","unstructured":"Buch, A. G., Kiforenko, L., & Kraft, D. (2017). Rotational subgroup voting and pose clustering for robust 3D object recognition. In Proceedings of the IEEE international conference on computer vision (pp.\u00a04137\u20134145). Los Alamitos: IEEE."},{"key":"22_CR58","first-page":"349","volume-title":"Proceedings of the fourth Pacific-Rim symposium on image and video technology","author":"F. Tombari","year":"2010","unstructured":"Tombari, F., & Di Stefano, L. (2010). Object recognition in 3D scenes with occlusions and clutter by hough voting. In Proceedings of the fourth Pacific-Rim symposium on image and video technology (pp.\u00a0349\u2013355). Los Alamitos: IEEE."},{"key":"22_CR59","unstructured":"Yang, J., Huang, Z., Quan, S., Zhang, Q., Zhang, Y., & Cao, Z. (2020). On efficient and robust metrics for ransac hypotheses and 3D rigid registration. arXiv preprint. arXiv:2011.04862."},{"key":"22_CR60","first-page":"4267","volume-title":"Proceedings of the IEEE conference on computer vision and pattern recognition","author":"S. Huang","year":"2021","unstructured":"Huang, S., Gojcic, Z., Usvyatsov, M., Wieser, A., & Schindler, K. (2021). Predator: registration of 3D point clouds with low overlap. In Proceedings of the IEEE conference on computer vision and pattern recognition (pp.\u00a04267\u20134276). Los Alamitos: IEEE."},{"key":"22_CR61","first-page":"766","volume-title":"Proceedings of the 14th European conference on computer vision","author":"Q.-Y. Zhou","year":"2016","unstructured":"Zhou, Q.-Y., Park, J., & Koltun, V. (2016). Fast global registration. In B. Leibe, J. Matas, N. Sebe, et al. (Eds.), Proceedings of the 14th European conference on computer vision (pp.\u00a0766\u2013782). Berlin: Springer."},{"key":"22_CR62","first-page":"2514","volume-title":"Proceedings of the IEEE conference on computer vision and pattern recognition","author":"C. Choy","year":"2020","unstructured":"Choy, C., Dong, W., & Koltun, V. (2020). Deep global registration. In Proceedings of the IEEE conference on computer vision and pattern recognition (pp.\u00a02514\u20132523). 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