[16] R. Jensen, A. Dahl, G. Vogiatzis, E. Tola, and H. Aanæs. Large scale multi-view stereopsis evaluation. In 2014 IEEE Conference on Computer Vision and Pattern Recognition, pages 406-413. IEEE, 2014.
[17] Y. Jiang, D. Ji, Z. Han, and M. Zwicker. Sdfdiff: Differentiable rendering of signed distance fields for 3d shape optimization. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 1251-1261, 2020.
[18] H. Kato, D. Beker, M. Morariu, T. Ando, T. Matsuoka, W. Kehl, and A. Gaidon. Differentiable rendering: A survey. CoRR, abs/2006.12057, 2020.
[19] M. Kazhdan and H. Hoppe. Screened poisson surface reconstruction. ACM Transactions on Graphics (ToG), 32(3):1-13, 2013.
[20] D. P. Kingma, J. A. Ba, and J. Adam. A method for stochastic optimization. arxiv 2014. arXiv preprint arXiv:1412.6980, 106, 2020.
[21] K. Li, J.-W. Bian, R. Castle, P. H. Torr, and V. A. Prisacariu. Mobilebrick: Building legs for 3d reconstruction on mobile devices. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 4892-4901, 2023.
[22] L. Liu, J. Gu, K. Zaw Lin, T.-S. Chua, and C. Theobalt. Neural sparse voxel fields. Advances in Neural Information Processing Systems, 33:15651-15663, 2020.
[23] S. Liu, Y. Zhang, S. Peng, B. Shi, M. Pollefeys, and Z. Cui. Dist: Rendering deep implicit signed distance function with differentiable sphere tracing. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 2019–2028, 2020.
[24] S. Lombardi, T. Simon, J. Saragih, G. Schwartz, A. Lehrmann, and Y. Sheikh. Neural volumes: Learning dynamic renderable volumes from images. arXiv preprint arXiv:1906.07751, 2019.
[25] X. Long, C. Lin, P. Wang, T. Komura, and W. Wang. SparseNeuS: Fast generalizable neural surface reconstruction from sparse views. In Computer Vision-ECCV 2022: 17th European Conference, Tel Aviv, Israel, October 23-27, 2022, Proceedings, Part XXXII, pages 210-227. Springer, 2022.
[26] W. E. Lorensen and H. E. Cline. Marching cubes: A high resolution 3d surface construction algorithm. ACM siggraph computer graphics, 21(4):163-169, 1987.
[27] W. Luo, A. G. Schwing, and R. Urtasun. Efficient deep learning for stereo matching. In Proceedings of the IEEE conference on computer vision and pattern recognition, pages 5695-5703, 2016.
[28] N. Max. Optical models for direct volume rendering. IEEE Transactions on Visualization and Computer Graphics, 1(2):99-108, 1995.
[29] L. Mescheder, M. Oechsle, M. Niemeyer, S. Nowozin, and A. 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.
[30] M. Michalkiewicz, J. K. Pontes, D. Jack, M. Baktashmotlagh, and A. Eriksson. Deep level sets: Implicit surface representations for 3d shape inference. arXiv preprint arXiv:1901.06802, 2019.
[31] B. Mildenhall, P. P. Srinivasan, M. Tancik, J. T. Barron, R. Ramamoorthi, and R. Ng. NeRF: Representing scenes as neural radiance fields for view synthesis. Communications of the ACM, 65(1):99-106, 2021.
[32] T. Müller, A. Evans, C. Schied, and A. Keller. Instant neural graphics primitives with a multiresolution hash encoding. ACM Transactions on Graphics (ToG), 41(4):1-15, 2022.
[33] M. Niemeyer, J. T. Barron, B. Mildenhall, M. S. Sajjadi, A. Geiger, and N. Radwan. RegNeRF: Regularizing neural radiance fields for view synthesis from sparse inputs. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 5480-5490, 2022.