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Recently, parametric mappings have emerged as highly effective surface representations, yielding low reconstruction error.
Semantic 3D Object Maps for Everyday Manipulation in Human Living Environments
R. B. Rusu · 2009
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Depth map prediction from a single image using a multi-scale deep network
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Shapenet: An information-rich 3d model repository
A. X. Chang, T. A. Funkhouser, L. J. Guibas, P. Hanrahan, Q.-X. Huang, Z. Li, S. Savarese, M. Savva, S. Song, H. Su, J. Xiao, L. Yi, and F. Yu · 2015
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Marr Revisited: 2D-3D model alignment via surface normal prediction
A. Bansal, B. Russell, and A. Gupta · 2016
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3d-r2n2: A unified approach for single and multi-view 3d object reconstruction
C. B. Choy, D. Xu, J. Gwak, K. Chen, and S. Savarese · 2016
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Deep Residual Learning for Image Recognition
K. He, X. Zhang, S. Ren, and J. Sun · 2016
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A point set generation network for 3d object reconstruction from a single image
H. Fan, H. Su, and L. J. Guibas · 2017
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Hierarchical surface prediction for 3d object reconstruction
C. Häne, S. Tulsiani, and J. Malik · 2017
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Pointnet: Deep learning on point sets for 3d classification and segmentation
C. R. Qi, H. Su, K. Mo, and L. J. Guibas · 2017
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PointNet++: Deep Hierarchical Feature Learning on Point Sets in a Metric Space
C. R. Qi, L. Yi, H. Su, and L. J. Guibas · 2017
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Learning to Reconstruct Texture-Less Deformable Surfaces
J. Bednarik, M. Salzmann, and P. Fua · 2018
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Atlasnet: A Papier-Mâché Approach to Learning 3D Surface Generation
T. Groueix, M. Fisher, V. Kim, B. Russell, and M. Aubry · 2018
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Pixel2mesh: Generating 3d mesh models from single rgb images
N. Wang, Y. Zhang, Z. Li, Y. Fu, W. Liu, and Y.-G. Jiang · 2018
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Foldingnet: Point Cloud Auto-Encoder via Deep Grid Deformation
Y. Yang, C. Feng, Y. Shen, and D. Tian · 2018
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Learning implicit fields for generative shape modeling
Z. Chen and H. Zhang · 2019
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Learning elementary structures for 3d shape generation and matching
T. Deprelle, T. Groueix, M. Fisher, V. Kim, B. Russell, and M. Aubry · 2019
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Mesh r-cnn
G. Gkioxari, J. Malik, and J. Johnson · 2019
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Garnet: A Two-Stream Network for Fast and Accurate 3D Cloth Draping
E. Gundogdu, V. Constantin, A. Seifoddini, M. Dang, M. Salzmann, and P. Fua · 2019
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Occupancy networks: Learning 3d reconstruction in function space
L. Mescheder, M. Oechsle, M. Niemeyer, S. Nowozin, and A. Geiger · 2019
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Shape unicode: A unified shape representation
S. Muralikrishnan, V. G. Kim, M. Fisher, and S. Chaudhuri · 2019
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Shape reconstruction by learning differentiable surface representations
J. Bednarik, S. Parashar, E. Gundogdu, M. Salzmann, and P. Fua · 2020
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Bsp-net: Generating compact meshes via binary space partitioning
Z. Chen, A. Tagliasacchi, and H. Zhang · 2020
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Limp: Learning latent shape representations with metric preservation priors
L. Cosmo, A. Norelli, O. Halimi, R. Kimmel, and E. Rodolà · 2020
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Cvxnet: Learnable convex decomposition
B. Deng, K. Genova, S. Yazdani, S. Bouaziz, G. Hinton, and A. Tagliasacchi · 2020
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Deep iterative surface normal estimation
J. E. Lenssen, C. Osendorfer, and J. Masci · 2020
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Tearingnet: Point cloud autoencoder to learn topology-friendly representations
J. Pang, D. Li, and D. Tian · 2020
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Deepsdf: Learning continuous signed distance functions for shape representation
J. J. Park, P. Florence, J. Straub, R. Newcombe, and S. Lovegrove · 2019
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Patch-based reconstruction of a textureless deformable 3d surface from a single rgb image
A. Tsoli and A. A. Argyros · 2019
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Deep geometric prior for surface reconstruction
F. Williams, T. Schneider, C. T. Silva, D. Zorin, J. Bruna, and D. Panozzo · 2019
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Disn: Deep implicit surface network for high-quality single-view 3d reconstruction
Q. Xu, W. Wang, D. Ceylan, R. Mech, and U. Neumann · 2019
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Deep surface normal estimation with hierarchical rgb-d fusion
J. Zeng, Y. Tong, Y. Huang, Q. Yan, W. Sun, J. Chen, and Y. Wang · 2019
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Sal: Sign agnostic learning of shapes from raw data
M. Atzmon and Y. Lipman · 2020
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Learning unsupervised hierarchical part decomposition of 3d objects from a single rgb image
D. Paschalidou, L. V. Gool, and A. Geiger · 2020
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Coupling explicit and implicit surface representations for generative 3d modeling
O. Poursaeed, M. Fisher, N. Aigerman, and V. G. Kim · 2020
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Inter-surface maps via constant-curvature metrics
P. Schmidt, M. Campen, J. Born, and L. Kobbelt · 2020
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Deep parametric shape predictions using distance fields
D. Smirnov, M. Fisher, V. G. Kim, R. Zhang, and J. Solomon · 2020
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Deep implicit volume compression
D. Tang, S. Singh, P. A. Chou, C. Hane, M. Dou, S. Fanello, J. Taylor, P. Davidson, O. G. Guleryuz, Y. Zhang, S. Izadi, A. Tagliasacchi, S. Bouaziz, and C. Keskin · 2020
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Front2back: Single view 3d shape reconstruction via front to back prediction
Y. Yao, N. Schertler, E. Rosales, H. Rhodin, L. Sigal, and A. Sheffer · 2020
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Neural cages for detail-preserving 3d deformations
W. Yifan, N. Aigerman, V. G. Kim, S. Chaudhuri, and O. Sorkine-Hornung · 2020
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