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Decomposing physically-based materials from images into their constituent properties remains challenging, particularly when maintaining both computational efficiency and physical consistency.
A reflectance model for computer graphics
Robert L Cook and Kenneth E. Torrance · 1982
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Perceptual losses for real-time style transfer and super-resolution
Justin Johnson, Alexandre Alahi, and Li Fei-Fei · 2016
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Single-image svbrdf capture with a rendering-aware deep network
Valentin Deschaintre, Miika Aittala, Fredo Durand, George Drettakis, and Adrien Bousseau · 2018
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Unsupervised learning for intrinsic image decomposition from a single image
Yunfei Liu, Yu Li, Shaodi You, and Feng Lu · 2020
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Towards robust monocular depth estimation: Mixing datasets for zero-shot cross-dataset transfer
René Ranftl, Katrin Lasinger, David Hafner, Konrad Schindler, and Vladlen Koltun · 2020
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Neural-pil: Neural pre-integrated lighting for reflectance decomposition
Mark Boss, Varun Jampani, Raphael Braun, Ce Liu, Jonathan Barron, and Hendrik Lensch · 2021
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Tm-net: Deep generative networks for textured meshes
Lin Gao, Tong Wu, Yu-Jie Yuan, Ming-Xian Lin, Yu-Kun Lai, and Hao Zhang · 2021
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Nerfactor: Neural factorization of shape and reflectance under an unknown illumination
Xiuming Zhang, Pratul P Srinivasan, Boyang Deng, Paul Debevec, William T Freeman, and Jonathan T Barron · 2021
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Nerfren: Neural radiance fields with reflections
Yuan-Chen Guo, Di Kang, Linchao Bao, Yu He, and Song-Hai Zhang · 2022
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Shape, light, and material decomposition from images using monte carlo rendering and denoising
Jon Hasselgren, Nikolai Hofmann, and Jacob Munkberg · 2022
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Clip-mesh: Generating textured meshes from text using pretrained image-text models
Nasir Mohammad Khalid, Tianhao Xie, Eugene Belilovsky, and Tiberiu Popa · 2022
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Extracting triangular 3d models, materials, and lighting from images
Jacob Munkberg, Jon Hasselgren, Tianchang Shen, Jun Gao, Wenzheng Chen, Alex Evans, Thomas Müller, and Sanja Fidler · 2022
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Dreamfusion: Text-to-3d using 2d diffusion
Ben Poole, Ajay Jain, Jonathan T Barron, and Ben Mildenhall · 2022
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High-resolution image synthesis with latent diffusion models
Robin Rombach, Andreas Blattmann, Dominik Lorenz, Patrick Esser, and Björn Ommer · 2022
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De-rendering 3d objects in the wild
Felix Wimbauer, Shangzhe Wu, and Christian Rupprecht · 2022
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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
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Wildlight: In-the-wild inverse rendering with a flashlight
Ziang Cheng, Junxuan Li, and Hongdong Li · 2023
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Objaverse: A universe of annotated 3d objects
Matt Deitke, Dustin Schwenk, Jordi Salvador, Luca Weihs, Oscar Michel, Eli VanderBilt, Ludwig Schmidt, Kiana Ehsani, Aniruddha Kembhavi, and Ali Farhadi · 2023
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Diffusiondepth: Diffusion denoising approach for monocular depth estimation
Yiqun Duan, Xianda Guo, and Zheng Zhu · 2023
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Relightable 3d gaussian: Real-time point cloud relighting with brdf decomposition and ray tracing
Jian Gao, Chun Gu, Youtian Lin, Hao Zhu, Xun Cao, Li Zhang, and Yao Yao · 2023
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Tensoir: Tensorial inverse rendering
Haian Jin, Isabella Liu, Peijia Xu, Xiaoshuai Zhang, Songfang Han, Sai Bi, Xiaowei Zhou, Zexiang Xu, and Hao Su · 2023
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Stanford-orb: a real-world 3d object inverse rendering benchmark
Zhengfei Kuang, Yunzhi Zhang, Hong-Xing Yu, Samir Agarwala, Elliott Wu, Jiajun Wu, et al · 2023
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Dinov2: Learning robust visual features without supervision
Maxime Oquab, Timothée Darcet, Théo Moutakanni, Huy Vo, Marc Szafraniec, Vasil Khalidov, Pierre Fernandez, Daniel Haziza, Francisco Massa, Alaaeldin El-Nouby, et al · 2023
Depthfm: Fast monocular depth estimation with flow matching
Ming Gui, Johannes S Fischer, Ulrich Prestel, Pingchuan Ma, Dmytro Kotovenko, Olga Grebenkova, Stefan Andreas Baumann, Vincent Tao Hu, and Björn Ommer · 2024
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Lotus: Diffusion-based visual foundation model for high-quality dense prediction
Jing He, Haodong Li, Wei Yin, Yixun Liang, Leheng Li, Kaiqiang Zhou, Hongbo Zhang, Bingbing Liu, and Ying-Cong Chen · 2024
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Repurposing diffusion-based image generators for monocular depth estimation
Bingxin Ke, Anton Obukhov, Shengyu Huang, Nando Metzger, Rodrigo Caye Daudt, and Konrad Schindler · 2024
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Intrinsic image diffusion for indoor single-view material estimation
Peter Kocsis, Vincent Sitzmann, and Matthias Nießner · 2024
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Gs-ir: 3d gaussian splatting for inverse rendering
Zhihao Liang, Qi Zhang, Ying Feng, Ying Shan, and Kui Jia · 2024
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Texture: Text-guided texturing of 3d shapes
Elad Richardson, Gal Metzer, Yuval Alaluf, Raja Giryes, and Daniel Cohen-Or · 2023
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Monocular depth estimation using diffusion models
Saurabh Saxena, Abhishek Kar, Mohammad Norouzi, and David J Fleet · 2023
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Matlaber: Material-aware text-to-3d via latent brdf auto-encoder
Xudong Xu, Zhaoyang Lyu, Xingang Pan, and Bo Dai · 2023
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Weakly-supervised single-view image relighting
Renjiao Yi, Chenyang Zhu, and Kai Xu · 2023
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Texture generation on 3d meshes with point-uv diffusion
Xin Yu, Peng Dai, Wenbo Li, Lan Ma, Zhengzhe Liu, and Xiaojuan Qi · 2023
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Unleashing text-to-image diffusion models for visual perception
Wenliang Zhao, Yongming Rao, Zuyan Liu, Benlin Liu, Jie Zhou, and Jiwen Lu · 2023
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Meta 3d texturegen: Fast and consistent texture generation for 3d objects
Raphael Bensadoun, Yanir Kleiman, Idan Azuri, Omri Harosh, Andrea Vedaldi, Natalia Neverova, and Oran Gafni · 2024
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Common diffusion noise schedules and sample steps are flawed
Shanchuan Lin, Bingchen Liu, Jiashi Li, and Xiao Yang · 2024
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Materialfusion: Enhancing inverse rendering with material diffusion priors
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Richdreamer: A generalizable normal-depth diffusion model for detail richness in text-to-3d
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Bojun Xiong, Jialun Liu, Jiakui Hu, Chenming Wu, Jinbo Wu, Xing Liu, Chen Zhao, Errui Ding, and Zhouhui Lian · 2024
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Diffusion models trained with large data are transferable visual models
Guangkai Xu, Yongtao Ge, Mingyu Liu, Chengxiang Fan, Kangyang Xie, Zhiyue Zhao, Hao Chen, and Chunhua Shen · 2024
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Stablenormal: Reducing diffusion variance for stable and sharp normal
Chongjie Ye, Lingteng Qiu, Xiaodong Gu, Qi Zuo, Yushuang Wu, Zilong Dong, Liefeng Bo, Yuliang Xiu, and Xiaoguang Han · 2024
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Paint-it: Text-to-texture synthesis via deep convolutional texture map optimization and physically-based rendering
Kim Youwang, Tae-Hyun Oh, and Gerard Pons-Moll · 2024
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Texgen: a generative diffusion model for mesh textures
Xin Yu, Ze Yuan, Yuan-Chen Guo, Ying-Tian Liu, Jianhui Liu, Yangguang Li, Yan-Pei Cao, Ding Liang, and Xiaojuan Qi · 2024
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Introducing the digital twin catalog from reality labs research, 2024
James Fort · 2025
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Geowizard: Unleashing the diffusion priors for 3d geometry estimation from a single image
Xiao Fu, Wei Yin, Mu Hu, Kaixuan Wang, Yuexin Ma, Ping Tan, Shaojie Shen, Dahua Lin, and Xiaoxiao Long · 2025
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