[1] Y. Y. Adikusuma, J. Du, Z. Fang, and Y. He. An accuracy controllable and memory efficient method for computing high-quality geodesic distances on triangle meshes. CAD, 150:103333, 2022.
[2] Y. Y. Adikusuma, Z. Fang, and Y. He. Fast construction of discrete geodesic graphs. TOG, 39(2):14:1-14:14, 2020.
[3] A. G. Belyaev and P.-A. Fayolle. On variational and pde-based distance function approximations. CGF, 34(8):104-118, 2015.
[4] O. Burghard and R. Klein. Simple, robust, constant-time bounds on surface geodesic distances using point landmarks. In VMV, pages 17-24, 2015.
[5] M. Campen and L. Kobbelt. Walking on broken mesh: Defect-tolerant geodesic distances and parameterizations. CGF, 30(2):623-632, 2011.
[6] R. Chabra, J. E. Lenssen, E. Ilg, T. Schmidt, J. Straub, S. Lovegrove, and R. Newcombe. Deep local shapes: Learning local sdf priors for detailed 3d reconstruction. In Proc. ECCV, pages 608-625, 2020.
[7] A. X. Chang, T. Funkhouser, L. Guibas, P. Hanrahan, Q. Huang, Z. Li, S. Savarese, M. Savva, S. Song, H. Su, et al. Shapenet: An information-rich 3d model repository. arXiv preprint arXiv:1512.03012, 2015.
[8] J. Chen and Y. Han. Shortest paths on a polyhedron. In SoCG, pages 360-369, 1990.
[9] Z. Chen and H. Zhang. Learning implicit fields for generative shape modeling. In Proc. CVPR, pages 5939-5948, 2019.
[10] K. Crane, C. Weischedel, and M. Wardetzky. Geodesics in heat: A new approach to computing distance based on heat flow. TOG, 32(5):152, 2013.
[11] M. Garland and P. S. Heckbert. Surface simplification using quadric error metrics. In SIGGRAPH, page 209-216, 1997.
[12] C. Gotsman and K. Hormann. Compressing geodesic information for fast point-to-point geodesic distance queries. In SIGGRAPH Asia, pages 1-9, 2022.
[13] T. Groueix, M. Fisher, V. G. Kim, B. C. Russell, and M. Aubry. A papier-mâché approach to learning 3d surface generation. In Proc. CVPR, pages 216-224, 2018.
[14] R. Hanocka, A. Hertz, N. Fish, R. Giryes, S. Fleishman, and D. Cohen-Or. MeshCNN: a network with an edge. TOG, 38(4):1-12, 2019.
[15] T. He, H. Huang, L. Yi, Y. Zhou, C. Wu, J. Wang, and S. Soatto. Geonet: Deep geodesic networks for point cloud analysis. In Proc. CVPR, pages 6888-6897, 2019.
[16] C. Jiang, A. Sud, A. Makadia, J. Huang, M. Nießner, T. Funkhouser, et al. Local implicit grid representations for 3d scenes. In Proc. CVPR, pages 6001-6010, 2020.
[17] R. Kimmel and J. A. Sethian. Computing geodesic paths on manifolds. *PNAS*, 95:8431–8435, 1998.
[18] Z. Li, X. TANG, Z. Xu, X. Wang, H. Yu, M. Chen, and X. Wei. Geodesic self-attention for 3d point clouds. In Proc. NeurIPS, 2022.
[19] C. Lin, N. Mitra, G. Wetzstein, L. J. Guibas, and P. Guerrero. Neuform: Adaptive overfitting for neural shape editing. Proc. NeurIPS, 35:15217-15229, 2022.
[20] I. Loshchilov and F. Hutter. Decoupled weight decay regularization. In Proc. ICLR, 2019.
[21] J. N. P. Martel, D. B. Lindell, C. Z. Lin, E. R. Chan, M. Monteiro, and G. Wetzstein. Acorn: Adaptive coordinate networks for neural scene representation. SIGGRAPH, 40(4), 2021.
[22] J. Masci, D. Boscaini, M. Bronstein, and P. Vandergheynst. Geodesic convolutional neural networks on riemannian manifolds. In Proc. ICCVW, pages 37-45, 2015.
[23] W. Meng, S. Xin, C. Tu, S. Chen, Y. He, and W. Wang. Geodesic tracks: Computing discrete geodesics with track-based steiner point propagation. TVCG, 28(12):4887-4901, 2022.
[24] L. Mescheder, M. Oechsle, M. Niemeyer, S. Nowozin, and A. Geiger. Occupancy networks: Learning 3d reconstruction in function space. In Proc. CVPR, pages 4460-4470, 2019.