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Multi-Layer Perceptrons (MLPs) make powerful functional representations for sampling and reconstruction problems involving low-dimensional signals like images,shapes and light fields.
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Neural networks with periodic and monotonic activation functions: a comparative study in classification problems
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Deep learning face attributes in the wild
Ziwei Liu, Ping Luo, Xiaogang Wang, and Xiaoou Tang · 2015
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Unsupervised representation learning with deep convolutional generative adversarial networks
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David Ha, Andrew Dai, and Quoc V Le · 2016
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Taming the waves: sine as activation function in deep neural networks
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A benchmark dataset and evaluation for non-lambertian and uncalibrated photometric stereo
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Ntire 2017 challenge on single image super-resolution: Dataset and study
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Feature visualization
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Deep reflectance volumes: Relightable reconstructions from multi-view photometric images
Sai Bi, Zexiang Xu, Kalyan Sunkavalli, Miloš Hašan, Yannick Hold-Geoffroy, David Kriegman, and Ravi Ramamoorthi · 2020
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Deep local shapes: Learning local sdf priors for detailed 3d reconstruction
Rohan Chabra, Jan Eric Lenssen, Eddy Ilg, Tanner Schmidt, Julian Straub, Steven Lovegrove, and Richard Newcombe · 2020
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Overfit neural networks as a compact shape representation
Thomas Davies, Derek Nowrouzezahrai, and Alec Jacobson · 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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Hypernetwork functional image representation
Sylwester Klocek, Łukasz Maziarka, Maciej Wołczyk, Jacek Tabor, Jakub Nowak, and Marek Śmieja · 2019
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Neural volumes: Learning dynamic renderable volumes from images
Stephen Lombardi, Tomas Simon, Jason Saragih, Gabriel Schwartz, Andreas Lehrmann, and Yaser Sheikh · 2019
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Deepsdf: Learning continuous signed distance functions for shape representation
Jeong Joon Park, Peter Florence, Julian Straub, Richard Newcombe, and Steven Lovegrove · 2019
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Deepvoxels: Learning persistent 3d feature embeddings
Vincent Sitzmann, Justus Thies, Felix Heide, Matthias Nießner, Gordon Wetzstein, and Michael Zollhöfer · 2019
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Scene representation networks: Continuous 3d-structure-aware neural scene representations
Vincent Sitzmann, Michael Zollhöfer, and Gordon Wetzstein · 2019
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Amos Gropp, Lior Yariv, Niv Haim, Matan Atzmon, and Yaron Lipman · 2020
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Dualsdf: Semantic shape manipulation using a two-level representation
Zekun Hao, Hadar Averbuch-Elor, Noah Snavely, and Serge Belongie · 2020
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Local implicit grid representations for 3d scenes
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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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X-fields: Implicit neural view-, light- and time-image interpolation
Hans-Peter Seidel Mojtaba Bemana, Karol Myszkowski and Tobias Ritschel · 2020
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Differentiable volumetric rendering: Learning implicit 3d representations without 3d supervision
Michael Niemeyer, Lars Mescheder, Michael Oechsle, and Andreas Geiger · 2020
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Graf: Generative radiance fields for 3d-aware image synthesis
Katja Schwarz, Yiyi Liao, Michael Niemeyer, and Andreas Geiger · 2020
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Metasdf: Meta-learning signed distance functions
Vincent Sitzmann, Eric Chan, Richard Tucker, Noah Snavely, and Gordon Wetzstein · 2020
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Implicit neural representations with periodic activation functions
Vincent Sitzmann, Julien NP 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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