Fetching the paper…
Reading the bibliography…
Diffusion models have emerged as the state-of-the-art for image generation, among other tasks.
Marching cubes: A high resolution 3D surface construction algorithm
William E Lorensen and Harvey E Cline · 1987
Earlier work this paper cites.
Marching cubes: A high resolution 3D surface construction algorithm
William E Lorensen and Harvey E Cline · 1987
Earlier work this paper cites.
The ball-pivoting algorithm for surface reconstruction
Fausto Bernardini, Joshua Mittleman, Holly E. Rushmeier, Cláudio T. Silva, and Gabriel Taubin · 1999
Earlier work this paper cites.
On visual similarity based 3d model retrieval
Ding-Yun Chen, Xiao-Pei Tian, Edward Yu-Te Shen, and Ming Ouhyoung · 2003
Earlier work this paper cites.
Fast image deconvolution using hyper-laplacian priors
Dilip Krishnan and Rob Fergus · 2009
Earlier work this paper cites.
Generative adversarial nets
Ian Goodfellow, Jean Pouget-Abadie, Mehdi Mirza, Bing Xu, David Warde-Farley, Sherjil Ozair, Aaron Courville, and Yoshua Bengio · 2014
Earlier work this paper cites.
Deep unsupervised learning using nonequilibrium thermodynamics
Jascha Sohl-Dickstein, Eric Weiss, Niru Maheswaranathan, and Surya Ganguli · 2015
Earlier work this paper cites.
Pixel recurrent neural networks
Aäron van den Oord, Nal Kalchbrenner, and Koray Kavukcuoglu · 2016
Earlier work this paper cites.
Learning a probabilistic latent space of object shapes via 3d generative-adversarial modeling
Jiajun Wu, Chengkai Zhang, Tianfan Xue, Bill Freeman, and Josh Tenenbaum · 2016
Earlier work this paper cites.
3d shape induction from 2d views of multiple objects
Matheus Gadelha, Subhransu Maji, and Rui Wang · 2017
Earlier work this paper cites.
Neural discrete representation learning
Aaron van den Oord, Oriol Vinyals, and koray kavukcuoglu · 2017
Earlier work this paper cites.
Neural scene representation and rendering
SM Ali Eslami, Danilo Jimenez Rezende, Frederic Besse, Fabio Viola, Ari S Morcos, Marta Garnelo, Avraham Ruderman, Andrei A Rusu, Ivo Danihelka, Karol Gregor, et al · 2018
Earlier work this paper cites.
Assessing generative models via precision and recall
Mehdi S. M. Sajjadi, Olivier Bachem, Mario Lucic, Olivier Bousquet, and Sylvain Gelly · 2018
Earlier work this paper cites.
Learning implicit fields for generative shape modeling
Zhiqin Chen and Hao Zhang · 2019
Earlier work this paper cites.
Learning shape templates with structured implicit functions
Kyle Genova, Forrester Cole, Daniel Vlasic, Aaron Sarna, William T Freeman, and Thomas Funkhouser · 2019
Earlier work this paper cites.
A style-based generator architecture for generative adversarial networks
Tero Karras, Samuli Laine, and Timo Aila · 2019
Earlier work this paper cites.
Learning to infer implicit surfaces without 3D supervision
Shichen Liu, Shunsuke Saito, Weikai Chen, and Hao Li · 2019
Earlier work this paper cites.
Neural volumes: Learning dynamic renderable volumes from images
Stephen Lombardi, Tomas Simon, Jason Saragih, Gabriel Schwartz, Andreas Lehrmann, and Yaser Sheikh · 2019
Earlier work this paper cites.
Occupancy networks: Learning 3D reconstruction in function space
Lars Mescheder, Michael Oechsle, Michael Niemeyer, Sebastian Nowozin, and Andreas Geiger · 2019
Earlier work this paper cites.
Implicit surface representations as layers in neural networks
Mateusz Michalkiewicz, Jhony K Pontes, Dominic Jack, Mahsa Baktashmotlagh, and Anders Eriksson · 2019
Earlier work this paper cites.
Hologan: Unsupervised learning of 3d representations from natural images
Thu Nguyen-Phuoc, Chuan Li, Lucas Theis, Christian Richardt, and Yong-Liang Yang · 2019
Earlier work this paper cites.
DeepSDF: Learning continuous signed distance functions for shape representation
Jeong Joon Park, Peter Florence, Julian Straub, Richard Newcombe, and Steven Lovegrove · 2019
Earlier work this paper cites.
Generating diverse high-fidelity images with vq-vae-2
Ali Razavi, Aaron van den Oord, and Oriol Vinyals · 2019
Earlier work this paper cites.
DeepVoxels: Learning persistent 3D feature embeddings
Vincent Sitzmann, Justus Thies, Felix Heide, Matthias Nießner, Gordon Wetzstein, and Michael Zollhöfer · 2019
Earlier work this paper cites.
Scene representation networks: Continuous 3D-structure-aware neural scene representations
Vincent Sitzmann, Michael Zollhöfer, and Gordon Wetzstein · 2019
Earlier work this paper cites.
Generative modeling by estimating gradients of the data distribution
Yang Song and Stefano Ermon · 2019
Earlier work this paper cites.
Occupancy networks: Learning 3d reconstruction in function space
Lars Mescheder, Michael Oechsle, Michael Niemeyer, Sebastian Nowozin, and Andreas Geiger · 2019
Earlier work this paper cites.
SAL: Sign agnostic learning of shapes from raw data
Matan Atzmon and Yaron Lipman · 2020
Earlier work this paper cites.
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
Earlier work this paper cites.
Overfit neural networks as a compact shape representation
Thomas Davies, Derek Nowrouzezahrai, and Alec Jacobson · 2020
Earlier work this paper cites.
Taming transformers for high-resolution image synthesis, 2020
Patrick Esser, Robin Rombach, and Björn Ommer · 2020
Earlier work this paper cites.
Local deep implicit functions for 3D shape
Kyle Genova, Forrester Cole, Avneesh Sud, Aaron Sarna, and Thomas Funkhouser · 2020
Cited alongside, same era.
Implicit geometric regularization for learning shapes
Amos Gropp, Lior Yariv, Niv Haim, Matan Atzmon, and Yaron Lipman · 2020
Cited alongside, same era.
Denoising diffusion probabilistic models
Jonathan Ho, Ajay Jain, and Pieter Abbeel · 2020
Cited alongside, same era.
Local implicit grid representations for 3D scenes
Chiyu Jiang, Avneesh Sud, Ameesh Makadia, Jingwei Huang, Matthias Nießner, and Thomas Funkhouser · 2020
Cited alongside, same era.
SDFDiff: Differentiable rendering of signed distance fields for 3D shape optimization
Yue Jiang, Dantong Ji, Zhizhong Han, and Matthias Zwicker · 2020
Cited alongside, same era.
Analyzing and improving the image quality of StyleGAN
Tero Karras, Samuli Laine, Miika Aittala, Janne Hellsten, Jaakko Lehtinen, and Timo Aila · 2020
Diffusion probabilistic models for 3d point cloud generation
Shitong Luo and Wei Hu · 2021
Later among the works it cites.
ACORN: Adaptive coordinate networks for neural representation
Julien N.P. Martel, David B. Lindell, Connor Z. Lin, Eric R. Chan, Marco Monteiro, and Gordon Wetzstein · 2021
Later among the works it cites.
NeRF in the wild: Neural radiance fields for unconstrained photo collections
Ricardo Martin-Brualla, Noha Radwan, Mehdi S. M. Sajjadi, Jonathan T. Barron, Alexey Dosovitskiy, and Daniel Duckworth · 2021
Later among the works it cites.
Modulated periodic activations for generalizable local functional representations
Ishit Mehta, Michaël Gharbi, Connelly Barnes, Eli Shechtman, Ravi Ramamoorthi, and Manmohan Chandraker · 2021
Later among the works it cites.
Gnerf: Gan-based neural radiance field without posed camera
Quan Meng, Anpei Chen, Haimin Luo, Minye Wu, Hao Su, Lan Xu, Xuming He, and Jingyi Yu · 2021
Later among the works it cites.
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
Cited alongside, same era.
Towards unsupervised learning of generative models for 3d controllable image synthesis
Yiyi Liao, Katja Schwarz, Lars Mescheder, and Andreas Geiger · 2020
Cited alongside, same era.
Neural sparse voxel fields
Lingjie Liu, Jiatao Gu, Kyaw Zaw Lin, Tat-Seng Chua, and Christian Theobalt · 2020
Cited alongside, same era.
DIST: Rendering deep implicit signed distance function with differentiable sphere tracing
Shaohui Liu, Yinda Zhang, Songyou Peng, Boxin Shi, Marc Pollefeys, and Zhaopeng Cui · 2020
Cited alongside, same era.
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
Cited alongside, same era.
Differentiable volumetric rendering: Learning implicit 3d representations without 3d supervision
Michael Niemeyer, Lars Mescheder, Michael Oechsle, and Andreas Geiger · 2020
Cited alongside, same era.
Convolutional occupancy networks
Songyou Peng, Michael Niemeyer, Lars Mescheder, Marc Pollefeys, and Andreas Geiger · 2020
Cited alongside, same era.
DONeRF: Towards Real-Time Rendering of Compact Neural Radiance Fields using Depth Oracle Networks
Thomas Neff, Pascal Stadlbauer, Mathias Parger, Andreas Kurz, Joerg H. Mueller, Chakravarty R. Alla Chaitanya, Anton S. Kaplanyan, and Markus Steinberger · 2021
Later among the works it cites.
Improved denoising diffusion probabilistic models
Alexander Quinn Nichol and Prafulla Dhariwal · 2021
Later among the works it cites.
Giraffe: Representing scenes as compositional generative neural feature fields
Michael Niemeyer and Andreas Geiger · 2021
Later among the works it cites.
UNISURF: Unifying neural implicit surfaces and radiance fields for multi-view reconstruction
Michael Oechsle, Songyou Peng, and Andreas Geiger · 2021
Later among the works it cites.
KiloNeRF: Speeding up neural radiance fields with thousands of tiny MLPs
Christian Reiser, Songyou Peng, Yiyi Liao, and Andreas Geiger · 2021
Later among the works it cites.
Denoising diffusion implicit models
Jiaming Song, Chenlin Meng, and Stefano Ermon · 2021
Later among the works it cites.
NeRV: Neural reflectance and visibility fields for relighting and view synthesis
Pratul P. Srinivasan, Boyang Deng, Xiuming Zhang, Matthew Tancik, Ben Mildenhall, and Jonathan T. Barron · 2021
Later among the works it cites.
Neural geometric level of detail: Real-time rendering with implicit 3D shapes
Towaki Takikawa, Joey Litalien, Kangxue Yin, Karsten Kreis, Charles Loop, Derek Nowrouzezahrai, Alec Jacobson, Morgan McGuire, and Sanja Fidler · 2021
Later among the works it cites.
Score-based generative modeling in latent space
Arash Vahdat, Karsten Kreis, and Jan Kautz · 2021
Later among the works it cites.
Dd-nerf: Double-diffusion neural radiance field as a generalizable implicit body representation
Guangming Yao, Hongzhi Wu, Yi Yuan, and Kun Zhou · 2021
Later among the works it cites.
PlenOctrees for real-time rendering of neural radiance fields
Alex Yu, Ruilong Li, Matthew Tancik, Hao Li, Ren Ng, and Angjoo Kanazawa · 2021
Later among the works it cites.
3d shape generation and completion through point-voxel diffusion
Linqi Zhou, Yilun Du, and Jiajun Wu · 2021
Later among the works it cites.
CIPS-3D: A 3D-Aware Generator of GANs Based on Conditionally-Independent Pixel Synthesis
Peng Zhou, Lingxi Xie, Bingbing Ni, and Qi Tian · 2021
Later among the works it cites.
Diffusion models beat gans on image synthesis
Prafulla Dhariwal and Alexander Nichol · 2021
Later among the works it cites.
3d shape generation with grid-based implicit functions
Moritz Ibing, Isaak Lim, and Leif P. Kobbelt · 2021
Later among the works it cites.
Denoising diffusion implicit models
Jiaming Song, Chenlin Meng, and Stefano Ermon · 2021
Later among the works it cites.
GAUDI: A neural architect for immersive 3d scene generation
Miguel Ángel Bautista, Pengsheng Guo, Samira Abnar, Walter Talbott, Alexander Toshev, Zhuoyuan Chen, Laurent Dinh, Shuangfei Zhai, Hanlin Goh, Daniel Ulbricht, Afshin Dehghan, and Josh M. Susskind · 2022
Closest in time.
POCO: point convolution for surface reconstruction
Alexandre Boulch and Renaud Marlet · 2022
Closest in time.
Efficient geometry-aware 3d generative adversarial networks
Eric R. Chan, Connor Z. Lin, Matthew A. Chan, Koki Nagano, Boxiao Pan, Shalini De Mello, Orazio Gallo, Leonidas J. Guibas, Jonathan Tremblay, Sameh Khamis, Tero Karras, and Gordon Wetzstein · 2022
Closest in time.
From data to functa: Your data point is a function and you should treat it like one
Emilien Dupont, Hyunjik Kim, S. M. Ali Eslami, Danilo J. Rezende, and Dan Rosenbaum · 2022
Closest in time.
Elucidating the design space of diffusion-based generative models
Tero Karras, Miika Aittala, Timo Aila, and Samuli Laine · 2022
Closest in time.
Stylesdf: High-resolution 3d-consistent image and geometry generation
Roy Or-El, Xuan Luo, Mengyi Shan, Eli Shechtman, Jeong Joon Park, and Ira Kemelmacher-Shlizerman · 2022
Closest in time.
Lolnerf: Learn from one look
Daniel Rebain, Mark Matthews, Kwang Moo Yi, Dmitry Lagun, and Andrea Tagliasacchi · 2022
Closest in time.
Advances in neural rendering
Ayush Tewari, Justus Thies, Ben Mildenhall, Pratul Srinivasan, Edgar Tretschk, W Yifan, Christoph Lassner, Vincent Sitzmann, Ricardo Martin-Brualla, Stephen Lombardi, et al · 2022
Closest in time.
Neural fields in visual computing and beyond
Yiheng Xie, Towaki Takikawa, Shunsuke Saito, Or Litany, Shiqin Yan, Numair Khan, Federico Tombari, James Tompkin, Vincent Sitzmann, and Srinath Sridhar · 2022
Closest in time.
Sdf-stylegan: Implicit sdf-based stylegan for 3d shape generation
Xin-Yang Zheng, Yang Liu, Peng-Shuai Wang, and Xin Tong · 2022
Closest in time.