[31] Chiyu Jiang, Avneesh Sud, Ameesh Makadia, Jingwei Huang, Matthias Nießner, Thomas Funkhouser, et al. Local implicit grid representations for 3d scenes. In CVPR, 2020. 3
[32] Hiroharu Kato, Yoshitaka Ushiku, and Tatsuya Harada. Neural 3d mesh renderer. In CVPR, 2018. 3
[33] Michael Kazhdan and Hugues Hoppe. Screened poisson surface reconstruction. TOG, 2013. 1, 3, 6
[34] Ravikrishna Kolluri. Provably good moving least squares. TALG, 2008. 3
[35] Tianyang Li, Xin Wen, Yu-Shen Liu, Hua Su, and Zhizhong Han. Learning deep implicit functions for 3d shapes with dynamic code clouds. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 12840-12850, 2022. 3
[36] Siyou Lin, Dong Xiao, Zuoqiang Shi, and Bin Wang. Surface reconstruction from point clouds without normals by parametrizing the gauss formula. ACM Transactions on Graphics, 42(2):1-19, 2022. 3
[37] Stefan Lionar, Daniil Emtsev, Dusan Svilarkovic, and Songyou Peng. Dynamic plane convolutional occupancy networks. In Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision, pages 1829-1838, 2021. 3
[38] Yaron Lipman. Phase transitions, distance functions, and implicit neural representations. In ICML, 2021. 3
[39] Chen Liu, Jimei Yang, Duygu Ceylan, Ersin Yumer, and Yasutaka Furukawa. Planenet: Piece-wise planar reconstruction from a single rgb image. In CVPR, 2018. 3
[40] Hsueh-Ti Derek Liu, Francis Williams, Alec Jacobson, Sanja Fidler, and Or Litany. Learning smooth neural functions via lipschitz regularization. arXiv preprint arXiv:2202.08345, 2022. 3
[41] Minghua Liu, Xiaoshuai Zhang, and Hao Su. Meshing point clouds with predicted intrinsic-extrinsic ratio guidance. In ECCV, 2020. 3
[42] Shi-Lin Liu, Hao-Xiang Guo, Hao Pan, Peng-Shuai Wang, Xin Tong, and Yang Liu. Deep implicit moving least-squares functions for 3d reconstruction. In CVPR, 2021. 3
[43] William E Lorensen and Harvey E Cline. Marching cubes: A high resolution 3d surface construction algorithm. In SIGGRAPH, 1987. 1, 3, 4
[44] Baorui Ma, Zhizhong Han, Yu-Shen Liu, and Matthias Zwicker. Neural-pull: Learning signed distance functions from point clouds by learning to pull space onto surfaces. In ICML, 2021. 3, 5, 6
[45] Baorui Ma, Yu-Shen Liu, and Zhizhong Han. Reconstructing surfaces for sparse point clouds with on-surface priors. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 6315-6325, 2022. 3
[46] Baorui Ma, Yu-Shen Liu, Matthias Zwicker, and Zhizhong Han. Surface reconstruction from point clouds by learning predictive context priors. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 6326-6337, 2022. 3
[47] Giacomo Meanti, Luigi Carratino, Ernesto De Vito, and Lorenzo Rosasco. Efficient hyperparameter tuning for large scale kernel ridge regression. In International Conference on Artificial Intelligence and Statistics, pages 6554-6572. PMLR, 2022. 2, 6
[48] Giacomo Meanti, Luigi Carratino, Lorenzo Rosasco, and Alessandro Rudi. Kernel methods through the roof: handling billions of points efficiently. Advances in Neural Information Processing Systems, 33:14410-14422, 2020. 2, 5
[49] Corentin Mercier, Thibault Lescoat, Pierre Roussillon, Tamy Boubekeur, and Jean-Marc Thiery. Moving level-of-detail surfaces. ACM Transactions on Graphics (TOG), 41(4):1-10, 2022. 3
[50] Lars Mescheder, Michael Oechsle, Michael Niemeyer, Sebastian Nowozin, and Andreas Geiger. Occupancy networks: Learning 3d reconstruction in function space. In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pages 4460-4470, 2019. 3
[51] Ben Mildenhall, Pratul P Srinivasan, Matthew Tancik, Jonathan T Barron, Ravi Ramamoorthi, and Ren Ng. Nerf: Representing scenes as neural radiance fields for view synthesis. In ECCV, 2020. 3
[52] Amine Ouasfi and Adnane Boukhayma. Few zero level set'-shot learning of shape signed distance functions in feature space. In ECCV, 2022. 3
[53] Amine Ouasfi and Adnane Boukhayma. Mixing-denoising generalizable occupancy networks. In 3DV, 2024. 3
[54] David Palmer, Dmitriy Smirnov, Stephanie Wang, Albert Chern, and Justin Solomon. Deepcurrents: Learning implicit representations of shapes with boundaries. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 18665-18675, 2022. 3
[55] Jeong Joon Park, Peter Florence, Julian Straub, Richard Newcombe, and Steven Lovegrove. Deepsdf: Learning continuous signed distance functions for shape representation. In CVPR, 2019. 3
[56] Songyou Peng, Chiyu Jiang, Yiyi Liao, Michael Niemeyer, Marc Pollefeys, and Andreas Geiger. Shape as points: A differentiable poisson solver. Advances in Neural Information Processing Systems, 34:13032-13044, 2021. 3, 5, 6
[57] Songyou Peng, Michael Niemeyer, Lars Mescheder, Marc Pollefeys, and Andreas Geiger. Convolutional occupancy networks. In European Conference on Computer Vision, pages 523-540. Springer, 2020. 3, 4, 5, 6, 7
[58] Charles R Qi, Hao Su, Kaichun Mo, and Leonidas J Guibas. Pointnet: Deep learning on point sets for 3d classification and segmentation. In CVPR, 2017. 3
[59] Marie-Julie Rakotosaona, Noam Aigerman, Niloy Mitra, Maks Ovsjanikov, and Paul Guerrero. Differentiable surface triangulation. In SIGGRAPH Asia, 2021. 3
[60] Gernot Riegler, Ali Osman Ulusoy, and Andreas Geiger. Octnet: Learning deep 3d representations at high resolutions. In CVPR, 2017. 3
[61] Bernhard Schölkopf, Joachim Giesen, and Simon Spalinger. Kernel methods for implicit surface modeling. In NeurIPS, 2004. 3