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Neural signed distance functions (SDFs) are emerging as an effective representation for 3D shapes.
A computer oriented geodetic data base and a new technique in file sequencing
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Ravish Mehra, Qingnan Zhou, Jeremy Long, Alla Sheffer, Amy Gooch, and Niloy J. Mitra · 2009
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Perception of simplification artifacts for animated characters
Michéal Larkin and Carol O’Sullivan · 2011
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Perceptual metrics for static and dynamic triangle meshes
Massimiliano Corsini, Mohamed-Chaker Larabi, Guillaume Lavoué, Oldřich Petřík, Libor Váša, and Kai Wang · 2013
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Adam: A method for stochastic optimization
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ShapeNet: An information-rich 3D model repository
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Weight normalization: A simple reparameterization to accelerate training of deep neural networks
Tim Salimans and Durk P Kingma · 2016
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Overfit neural networks as a compact shape representation
Thomas Davies, Derek Nowrouzezahrai, and Alec Jacobson · 2020
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CvxNet: Learnable convex decomposition
Boyang Deng, Kyle Genova, Soroosh Yazdani, Sofien Bouaziz, Geoffrey Hinton, and Andrea Tagliasacchi · 2020
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Yueqi Duan, Haidong Zhu, He Wang, Li Yi, Ram Nevatia, and Leonidas J. Guibas · 2020
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Learning deformable tetrahedral meshes for 3D reconstruction
Jun Gao, Wenzheng Chen, Tommy Xiang, Alec Jacobson, Morgan McGuire, and Sanja Fidler · 2020
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Local deep implicit functions for 3D shape
Kyle Genova, Forrester Cole, Avneesh Sud, Aaron Sarna, and Thomas Funkhouser · 2020
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Learning implicit fields for generative shape modeling
Zhiqin Chen and Hao Zhang · 2019
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SDFDiff: Differentiable rendering of signed distance fields for 3D shape optimization
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Neural sparse voxel fields
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DIST: Rendering deep implicit signed distance function with differentiable sphere tracing
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NeRF: Representing scenes as neural radiance fields for view synthesis
Ben Mildenhall, Pratul P. Srinivasan, Matthew Tancik, Jonathan T. Barron, Ravi Ramamoorthi, and Ren Ng · 2020
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Differentiable volumetric rendering: Learning implicit 3D representations without 3D supervision
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Accelerating 3d deep learning with pytorch3d
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Implicit neural representations with periodic activation functions
Vincent Sitzmann, Julien N. P. Martel, Alexander W. Bergman, David B. Lindell, and Gordon Wetzstein · 2020
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Fourier features let networks learn high frequency functions in low dimensional domains
Matthew Tancik, Pratul P. Srinivasan, Ben Mildenhall, Sara Fridovich-Keil, Nithin Raghavan, Utkarsh Singhal, Ravi Ramamoorthi, Jonathan T. Barron, and Ren Ng · 2020
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PatchNets: Patch-based generalizable deep implicit 3D shape representations
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Multiview neural surface reconstruction with implicit lighting and material
Lior Yariv, Yoni Kasten, Dror Moran, Meirav Galun, Matan Atzmon, Ronen Basri, and Yaron Lipman · 2020
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