Fetching the paper…
Reading the bibliography…
We present Intrinsic Image Diffusion, a generative model for appearance decomposition of indoor scenes.
Lightness and retinex theory
Edwin Land and John McCann · 1971
Earlier work this paper cites.
Microfacet models for refraction through rough surfaces
Bruce Walter, Stephen R. Marschner, Hongsong Li, and Kenneth E. Torrance · 2007
Earlier work this paper cites.
Ground truth dataset and baseline evaluations for intrinsic image algorithms
Roger B. Grosse, Micah K. Johnson, Edward H. Adelson, and William T. Freeman · 2009
Earlier work this paper cites.
Denoising diffusion implicit models
Jiaming Song, Chenlin Meng, and Stefano Ermon · 2010
Earlier work this paper cites.
Score-based generative modeling through stochastic differential equations
Yang Song, Jascha Sohl-Dickstein, Diederik P. Kingma, Abhishek Kumar, Stefano Ermon, and Ben Poole · 2011
Earlier work this paper cites.
Intrinsic images by clustering
Elena Garces, Adolfo Munoz, Jorge Lopez-Moreno, and Diego Gutierrez · 2012
Earlier work this paper cites.
Intrinsic scene properties from a single RGB-D image
Jonathan T. Barron and Jitendra Malik · 2013
Earlier work this paper cites.
A simple model for intrinsic image decomposition with depth cues
Qifeng Chen and Vladlen Koltun · 2013
Earlier work this paper cites.
Intrinsic images in the wild
Sean Bell, Kavita Bala, and Noah Snavely · 2014
Earlier work this paper cites.
Shape, illumination, and reflectance from shading
Jonathan T. Barron and Jitendra Malik · 2015
Earlier work this paper cites.
Adam: A method for stochastic optimization
Diederik P. Kingma and Jimmy Ba · 2015
Earlier work this paper cites.
U-net: Convolutional networks for biomedical image segmentation
Olaf Ronneberger, Philipp Fischer, and Thomas Brox · 2015
Earlier work this paper cites.
Deep unsupervised learning using nonequilibrium thermodynamics
Jascha Sohl-Dickstein, Eric A. Weiss, Niru Maheswaranathan, and Surya Ganguli · 2015
Earlier work this paper cites.
Image-to-image translation with conditional adversarial networks
Phillip Isola, Jun-Yan Zhu, Tinghui Zhou, and Alexei A Efros · 2017
Earlier work this paper cites.
Shading annotations in the wild
Balazs Kovacs, Sean Bell, Noah Snavely, and Kavita Bala · 2017
Earlier work this paper cites.
Cgintrinsics: Better intrinsic image decomposition through physically-based rendering
Zhengqi Li and Noah Snavely · 2018
Earlier work this paper cites.
Learning intrinsic image decomposition from watching the world
Zhengqi Li and Noah Snavely · 2018
Earlier work this paper cites.
Learning to reconstruct shape and spatially-varying reflectance from a single image
Zhengqin Li, Zexiang Xu, Ravi Ramamoorthi, Kalyan Sunkavalli, and Manmohan Chandraker · 2018
Cited alongside, same era.
Inverse path tracing for joint material and lighting estimation
Dejan Azinovic, Tzu-Mao Li, Anton Kaplanyan, and Matthias Nießner · 2019
Cited alongside, same era.
Decoupled weight decay regularization
Ilya Loshchilov and Frank Hutter · 2019
Cited alongside, same era.
Denoising diffusion probabilistic models
Jonathan Ho, Ajay Jain, and Pieter Abbeel · 2020
Cited alongside, same era.
Inverse rendering for complex indoor scenes: Shape, spatially-varying lighting and SVBRDF from a single image
Zhengqin Li, Mohammad Shafiei, Ravi Ramamoorthi, Kalyan Sunkavalli, and Manmohan Chandraker · 2020
Cited alongside, same era.
Omnidata: A scalable pipeline for making multi-task mid-level vision datasets from 3d scans
LAION-5B: an open large-scale dataset for training next generation image-text models
Christoph Schuhmann, Romain Beaumont, Richard Vencu, Cade Gordon, Ross Wightman, Mehdi Cherti, Theo Coombes, Aarush Katta, Clayton Mullis, Mitchell Wortsman, Patrick Schramowski, Srivatsa Kundurthy, Katherine Crowson, Ludwig Schmidt, Robert Kaczmarczyk, and Jenia Jitsev · 2022
Later among the works it cites.
Diffusion models: A comprehensive survey of methods and applications
Ling Yang, Zhilong Zhang, Yang Song, Shenda Hong, Runsheng Xu, Yue Zhao, Yingxia Shao, Wentao Zhang, Ming-Hsuan Yang, and Bin Cui · 2022
Later among the works it cites.
Learning-based inverse rendering of complex indoor scenes with differentiable monte carlo raytracing
Jingsen Zhu, Fujun Luan, Yuchi Huo, Zihao Lin, Zhihua Zhong, Dianbing Xi, Rui Wang, Hujun Bao, Jiaxiang Zheng, and Rui Tang · 2022
Later among the works it cites.
Irisformer: Dense vision transformers for single-image inverse rendering in indoor scenes
Rui Zhu, Zhengqin Li, Janarbek Matai, Fatih Porikli, and Manmohan Chandraker · 2022
Later among the works it cites.
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
Ainaz Eftekhar, Alexander Sax, Jitendra Malik, and Amir Zamir · 2021
Cited alongside, same era.
Openclip, 2021
Gabriel Ilharco, Mitchell Wortsman, Ross Wightman, Cade Gordon, Nicholas Carlini, Rohan Taori, Achal Dave, Vaishaal Shankar, Hongseok Namkoong, John Miller, Hannaneh Hajishirzi, Ali Farhadi, and Ludwig Schmidt · 2021
Cited alongside, same era.
Diederik P. Kingma, Tim Salimans, Ben Poole, and Jonathan Ho · 2021
Cited alongside, same era.
Material and lighting reconstruction for complex indoor scenes with texture-space differentiable rendering
Merlin Nimier-David, Zhao Dong, Wenzel Jakob, and Anton Kaplanyan · 2021
Cited alongside, same era.
Free-viewpoint indoor neural relighting from multi-view stereo
Julien Philip, Sébastien Morgenthaler, Michaël Gharbi, and George Drettakis · 2021
Cited alongside, same era.
Learning transferable visual models from natural language supervision
Alec Radford, Jong Wook Kim, Chris Hallacy, A. Ramesh, Gabriel Goh, Sandhini Agarwal, Girish Sastry, Amanda Askell, Pamela Mishkin, Jack Clark, Gretchen Krueger, and Ilya Sutskever · 2021
Cited alongside, same era.
High-resolution image synthesis with latent diffusion models
Robin Rombach, Andreas Blattmann, Dominik Lorenz, Patrick Esser, and Björn Ommer · 2021
Cited alongside, same era.
Anand Bhattad and D.A. Forsyth · 2023
Closest in time.
Stylegan knows normal, depth, albedo, and more
Anand Bhattad, Daniel McKee, Derek Hoiem, and David A. Forsyth · 2023
Closest in time.
MAIR: multi-view attention inverse rendering with 3d spatially-varying lighting estimation
Junyong Choi, SeokYeong Lee, Haesol Park, Seung-Won Jung, Ig-Jae Kim, and Junghyun Cho · 2023
Closest in time.
Generative models: What do they know? do they know things? let’s find out!
Xiaodan Du, Nicholas I. Kolkin, Greg Shakhnarovich, and Anand Bhattad · 2023
Closest in time.
Estimating reflectance layer from a single image: Integrating reflectance guidance and shadow/specular aware learning
Yeying Jin, Ruoteng Li, Wenhan Yang, and Robby T Tan · 2023
Closest in time.
Exploiting diffusion prior for generalizable pixel-level semantic prediction
Hsin-Ying Lee, Hung-Yu Tseng, Hsin-Ying Lee, and Ming-Hsuan Yang · 2023
Closest in time.
Zero-1-to-3: Zero-shot one image to 3d object
Ruoshi Liu, Rundi Wu, Basile Van Hoorick, Pavel Tokmakov, Sergey Zakharov, and Carl Vondrick · 2023
Closest in time.
State of the art on diffusion models for visual computing
Ryan Po, Wang Yifan, Vladislav Golyanik, Kfir Aberman, Jonathan T. Barron, Amit H. Bermano, Eric Ryan Chan, Tali Dekel, Aleksander Holynski, Angjoo Kanazawa, C. Karen Liu, Lingjie Liu, Ben Mildenhall, Matthias Nießner, Björn Ommer, Christian Theobalt, Peter Wonka, and Gordon Wetzstein · 2023
Closest in time.
Umat: Uncertainty-aware single image high resolution material capture
Carlos Rodríguez-Pardo, Henar Dominguez-Elvira, David Pascual-Hernández, and Elena Garces · 2023
Closest in time.
Measured albedo in the wild: Filling the gap in intrinsics evaluation
Jiaye Wu, Sanjoy Chowdhury, Hariharmano Shanmugaraja, David Jacobs, and Soumyadip Sengupta · 2023
Closest in time.
Scannet++: A high-fidelity dataset of 3d indoor scenes
Chandan Yeshwanth, Yueh-Cheng Liu, Matthias Nießner, and Angela Dai · 2023
Closest in time.
Adding conditional control to text-to-image diffusion models
Lvmin Zhang and Maneesh Agrawala · 2023
Closest in time.