[25] Sijia Jiang, Jing Hua, and Zhizhong Han. Coordinate quantized neural implicit representations for multi-view 3d reconstruction. In IEEE International Conference on Computer Vision, 2023.
[26] Yue Jiang, Dantong Ji, Zhizhong Han, and Matthias Zwicker. SDFDiff: Differentiable rendering of signed distance fields for 3D shape optimization. In IEEE Conference on Computer Vision and Pattern Recognition, 2020.
[27] Leonid Keselman and Martial Hebert. Flexible Techniques for Differentiable Rendering with 3D Gaussians, 2023.
[28] Lukas Koestler, Nan Yang, Niclas Zeller, and Daniel Cremers. TANDEM: tracking and dense mapping in real-time using deep multi-view stereo. In Aleksandra Faust, David Hsu, and Gerhard Neumann, editors, Conference on Robot Learning, volume 164, pages 34-45, 2021.
[29] Xin Kong, Shikun Liu, Marwan Taher, and Andrew J Davison. vmap: Vectorised object mapping for neural field slam. arXiv preprint arXiv:2302.01838, 2023.
[30] A. Laurentini. The visual hull concept for silhouette-based image understanding. IEEE Transactions on Pattern Analysis and Machine Intelligence, 16(2):150-162, 1994.
[31] Seunghwan Lee, Gwanmo Park, Hyewon Son, Jiwon Ryu, and Han Joo Chae. Fastsurf: Fast neural rgb-d surface reconstruction using per-frame intrinsic refinement and tsdf fusion prior learning. arXiv preprint arXiv:2303.04508, 2023.
[32] Jiabao Lei, Jiapeng Tang, and Kui Jia. Rgbd2: Generative scene synthesis via incremental view inpainting using rgbd diffusion models. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), June 2023.
[33] Tianyang Li, Xin Wen, Yu-Shen Liu, Hua Su, and Zhizhong Han. Learning deep implicit functions for 3d shapes with dynamic code clouds. In IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2022.
[34] Zhaoshuo Li, Thomas Müller, Alex Evans, Russell H Taylor, Mathias Unberath, Ming-Yu Liu, and Chen-Hsuan Lin. Neuralangelo: High-fidelity neural surface reconstruction. In IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2023.
[35] Zhihao Liang, Zhangjin Huang, Changxing Ding, and Kui Jia. Helixsurf: A robust and efficient neural implicit surface learning of indoor scenes with iterative intertwined regularization, 2023.
[36] Chen-Hsuan Lin, Chaoyang Wang, and Simon Lucey. SDF-SRN: Learning signed distance 3D object reconstruction from static images. In Advances in Neural Information Processing Systems, 2020.
[37] Shichen Liu, Shunsuke Saito, Weikai Chen, and Hao Li. Learning to infer implicit surfaces without 3D supervision. In Advances in Neural Information Processing Systems, 2019.
[38] Shaohui Liu, Yinda Zhang, Songyou Peng, Boxin Shi, Marc Pollefeys, and Zhaopeng Cui. DIST: Rendering deep implicit signed distance function with differentiable sphere tracing. In IEEE Conference on Computer Vision and Pattern Recognition, 2020.
[39] Shi-Lin Liu, Hao-Xiang Guo, Hao Pan, Pengshuai Wang, Xin Tong, and Yang Liu. Deep implicit moving least-squares functions for 3D reconstruction. In IEEE Conference on Computer Vision and Pattern Recognition, 2021.
[40] William E. Lorensen and Harvey E. Cline. Marching cubes: A high resolution 3D surface construction algorithm. Computer Graphics, 21(4):163-169, 1987.
[41] 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 International Conference on Machine Learning, 2021.
[42] Baorui Ma, Yu-Shen Liu, and Zhizhong Han. Learning signed distance functions from noisy 3d point clouds via noise to noise mapping. In International Conference on Machine Learning (ICML), 2023.
[43] Baorui Ma, Yu-Shen Liu, Matthias Zwicker, and Zhizhong Han. Surface reconstruction from point clouds by learning predictive context priors. In IEEE Conference on Computer Vision and Pattern Recognition, 2022.
[44] Baorui Ma, Junsheng Zhou, Yu-Shen Liu, and Zhizhong Han. Towards better gradient consistency for neural signed distance functions via level set alignment. In Conference on Computer Vision and Pattern Recognition (CVPR), 2023.
[45] Julien N. P. Martel, David B. Lindell, Connor Z. Lin, Eric R. Chan, Marco Monteiro, and Gordon Wetzstein. ACORN: adaptive coordinate networks for neural scene representation. CoRR, abs/2105.02788, 2021.
[46] 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 European Conference on Computer Vision, 2020.
[47] Thomas Müller, Alex Evans, Christoph Schied, and Alexander Keller. Instant neural graphics primitives with a multiresolution hash encoding. arXiv:2201.05989, 2022.
[48] Jacob Munkberg, Jon Hasselgren, Tianchang Shen, Jun Gao, Wenzheng Chen, Alex Evans, Thomas Müller, and Sanja Fidler. Extracting Triangular 3D Models, Materials, and Lighting From Images. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), pages 8280-8290, June 2022.