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Meshes are important representations of physical 3D entities in the virtual world.
M. K. Fort, “The embedding of homeomorphisms in flows,”
1955
Earlier work this paper cites.
W. E. Lorensen and H. E. Cline, “Marching cubes: A high resolution 3D surface construction algorithm,”
1987
Earlier work this paper cites.
J. Arango and A. Gómez, “Flows and diffeomorphisms,”
1998
Earlier work this paper cites.
S. Raman and R. Wenger, “Quality isosurface mesh generation using an extended marching cubes lookup table,” in
2008
Earlier work this paper cites.
Focal Press, 2010
R. Hess, · 2010
Earlier work this paper cites.
T. Karras, “Maximizing parallelism in the construction of bvhs, octrees, and k-d trees,” in
2012
Earlier work this paper cites.
D. P. Kingma and J. Ba, “Adam: A method for stochastic optimization,”
2014
Earlier work this paper cites.
Z. Wu, S. Song, A. Khosla, F. Yu, L. Zhang, X. Tang, and J. Xiao, “3D shapenets: A deep representation for volumetric shapes,” in
2015
Earlier work this paper cites.
S. Ioffe and C. Szegedy, “Batch normalization: Accelerating deep network training by reducing internal covariate shift,” in
2015
Earlier work this paper cites.
2015
Earlier work this paper cites.
C. B. Choy, D. Xu, J. Gwak, K. Chen, and S. Savarese, “3D-R2N2: A unified approach for single and multi-view 3D object reconstruction,” in
2016
Earlier work this paper cites.
R. Girdhar, D. F. Fouhey, M. Rodriguez, and A. Gupta, “Learning a predictable and generative vector representation for objects,” in
2016
Earlier work this paper cites.
W. Wen, C. Wu, Y. Wang, Y. Chen, and H. Li, “Learning structured sparsity in deep neural networks,” in
2016
Earlier work this paper cites.
T. Rashid, S. Sultana, and M. A. Audette, “Watertight and 2-manifold surface meshes using dual contouring with tetrahedral decomposition of grid cubes,”
2016
Earlier work this paper cites.
J. L. Ba, J. R. Kiros, and G. E. Hinton, “Layer normalization,”
2016
Earlier work this paper cites.
T. Salimans and D. P. Kingma, “Weight normalization: A simple reparameterization to accelerate training of deep neural networks,” in
2016
Earlier work this paper cites.
D. Ulyanov, V. Lebedev, A. Vedaldi, and V. S. Lempitsky, “Texture networks: Feed-forward synthesis of textures and stylized images.,” in
2016
Earlier work this paper cites.
K. He, X. Zhang, S. Ren, and J. Sun, “Identity mappings in deep residual networks,” in
2016
Earlier work this paper cites.
H. Fan, H. Su, and L. J. Guibas, “A point set generation network for 3D object reconstruction from a single image,” in
2017
Earlier work this paper cites.
J. Wu, Y. Wang, T. Xue, X. Sun, B. Freeman, and J. Tenenbaum, “Marrnet: 3D shape reconstruction via 2.5 d sketches,” in
2017
Cited alongside, same era.
C. Häne, S. Tulsiani, and J. Malik, “Hierarchical surface prediction for 3D object reconstruction,” in
2017
Cited alongside, same era.
M. Tatarchenko, A. Dosovitskiy, and T. Brox, “Octree generating networks: Efficient convolutional architectures for high-resolution 3D outputs,” in
2017
Cited alongside, same era.
S. Zuffi, A. Kanazawa, D. W. Jacobs, and M. J. Black, “3D menagerie: Modeling the 3D shape and pose of animals,” in
2017
Cited alongside, same era.
C. R. Qi, H. Su, K. Mo, and L. J. Guibas, “Pointnet: Deep learning on point sets for 3D classification and segmentation,” in
2017
Cited alongside, same era.
L. Mescheder, M. Oechsle, M. Niemeyer, S. Nowozin, and A. Geiger, “Occupancy networks: Learning 3D reconstruction in function space,” in
2019
Later among the works it cites.
E. Dupont, A. Doucet, and Y. W. Teh, “Augmented neural odes,” in
2019
Later among the works it cites.
M. Niemeyer, L. Mescheder, M. Oechsle, and A. Geiger, “Occupancy flow: 4d reconstruction by learning particle dynamics,” in
2019
Later among the works it cites.
G. Yang, X. Huang, Z. Hao, M.-Y. Liu, S. Belongie, and B. Hariharan, “Pointflow: 3D point cloud generation with continuous normalizing flows,” in
2019
Later among the works it cites.
J. J. Park, P. Florence, J. Straub, R. Newcombe, and S. Lovegrove, “Deepsdf: Learning continuous signed distance functions for shape representation,” in
2019
Later among the works it cites.
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T. Q. Chen, Y. Rubanova, J. Bettencourt, and D. K. Duvenaud, “Neural ordinary differential equations,” in
2018
Cited alongside, same era.
T. Groueix, M. Fisher, V. G. Kim, B. C. Russell, and M. Aubry, “Atlasnet: A papier-mâché approach approach to learning 3D surface generation,” in
2018
Cited alongside, same era.
N. Wang, Y. Zhang, Z. Li, Y. Fu, W. Liu, and Y.-G. Jiang, “Pixel2mesh: Generating 3D mesh models from single RGB images,” in
2018
Cited alongside, same era.
J. Wu, C. Zhang, X. Zhang, Z. Zhang, W. T. Freeman, and J. B. Tenenbaum, “Learning shape priors for single-view 3D completion and reconstruction,” in
2018
Cited alongside, same era.
X. Zhang, Z. Zhang, C. Zhang, J. Tenenbaum, B. Freeman, and J. Wu, “Learning to reconstruct shapes from unseen classes,” in
2018
Cited alongside, same era.
Y. Yang, C. Feng, Y. Shen, and D. Tian, “Foldingnet: Point cloud auto-encoder via deep grid deformation,” in
2018
Cited alongside, same era.
S. Zuffi, A. Kanazawa, and M. J. Black, “Lions and tigers and bears: Capturing non-rigid, 3D, articulated shape from images,” in
2018
Cited alongside, same era.
2019
Later among the works it cites.
Z. Chen and H. Zhang, “Learning implicit fields for generative shape modeling,” in
2019
Later among the works it cites.
L. Custodio, S. Pesco, and C. Silva, “An extended triangulation to the marching cubes 33 algorithm,”
2019
Later among the works it cites.
N. Kolotouros, G. Pavlakos, and K. Daniilidis, “Convolutional mesh regression for single-image human shape reconstruction,” in
2019
Later among the works it cites.
W. Wang, D. Ceylan, R. Mech, and U. Neumann, “3DN: 3D deformation network,” in
2019
Later among the works it cites.
Y. Hanshu, D. Jiawei, T. Vincent, and F. Jiashi, “On robustness of neural ordinary differential equations,” in
2019
Later among the works it cites.
W. Grathwohl, R. T. Q. Chen, J. Bettencourt, I. Sutskever, and D. Duvenaud, “Ffjord: Free-form continuous dynamics for scalable reversible generative models,”
2019
Later among the works it cites.
M. Nimier-David, D. Vicini, T. Zeltner, and W. Jakob, “Mitsuba 2: A retargetable forward and inverse renderer,”
2019
Later among the works it cites.
H. Zhang, X. Gao, J. Unterman, and T. Arodz, “Approximation capabilities of neural odes and invertible residual networks,” in
2020
Closest in time.
S. Massaroli, M. Poli, J. Park, A. Yamashita, and H. Asama, “Dissecting neural odes,”
2020
Closest in time.
R. Chabra, J. E. Lenssen, E. Ilg, T. Schmidt, J. Straub, S. Lovegrove, and R. A. Newcombe, “Deep local shapes: Learning local SDF priors for detailed 3d reconstruction,” in
2020
Closest in time.
P. Henderson, V. Tsiminaki, and C. H. Lampert, “Leveraging 2D data to learn textured 3D mesh generation,” in
2020
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N. Ravi, J. Reizenstein, D. Novotny, T. Gordon, W.-Y. Lo, J. Johnson, and G. Gkioxari, “Pytorch3D.”
2020
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B. Deng, K. Genova, S. Yazdani, S. Bouaziz, G. Hinton, and A. Tagliasacchi, “Cvxnet: Learnable convex decomposition,” in
2020
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