De-Rendering the World's Revolutionary Artefacts
Shangzhe Wu, Ameesh Makadia, Jiajun Wu, Noah Snavely, Richard Tucker, Angjoo Kanazawa
2021Year
15Top-tier citations
Abstract
Input Albedo Diffuse Specular Material Env. map Normal Novel view Single image collection Training Inference Relight Figure 1: De-rendering from single images. From only a real single-view image collection of "revolutionary" (i.e., solid of revolution) artefacts with known silhouettes as training data (left), our framework learns to de-render a single image into shape, albedo and complex lighting and material components, suitable for applications such as novel-view synthesis and relighting (right).
Ask about this paper
Your agent reads all of it.
Lune indexed this paper to the last equation, along with the top-tier papers that cite it. Ask a question and the answer quotes them.
Cited by top-tier papers15
- NeRS: Neural Reflectance Surfaces for Sparse-view 3D Reconstruction in the WildJason Y. Zhang, Gengshan Yang, Shubham Tulsiani, Deva RamananNeurIPS 2021 · 180 citations
- DIB-R++: Learning to Predict Lighting and Material with a Hybrid Differentiable RendererWenzheng Chen, Joey Litalien, Jun Gao, Zian Wang et al.NeurIPS 2021 · 74 citations
- De-rendering 3D Objects in the WildFelix Wimbauer, Shangzhe Wu, Christian RupprechtCVPR 2022 · 29 citations
- Industrial Style Transfer with Large-scale Geometric Warping and Content PreservationJinchao Yang, Fei Guo, Shuo Chen, Jun Li et al.CVPR 2022 · 16 citations
- SimNP: Learning Self-Similarity Priors Between Neural PointsChristopher Wewer, Eddy Ilg, Bernt Schiele, Jan Eric LenssenICCV 2023 · 11 citations
Builds on5
- Multiview Neural Surface Reconstruction by Disentangling Geometry and AppearanceLior Yariv, Yoni Kasten, Dror Moran, Meirav Galun et al.NeurIPS 2020 · 1,010 citations
- Inverse Rendering for Complex Indoor Scenes: Shape, Spatially-Varying Lighting and SVBRDF From a Single ImageZhengqin Li, Mohammad Shafiei, Ravi Ramamoorthi, Kalyan Sunkavalli et al.CVPR 2020
- Unsupervised Learning of Probably Symmetric Deformable 3D Objects From Images in the WildShangzhe Wu, Christian Rupprecht, Andrea VedaldiCVPR 2020
- Deep 3D Capture: Geometry and Reflectance From Sparse Multi-View ImagesSai Bi, Zexiang Xu, Kalyan Sunkavalli, David J. Kriegman et al.CVPR 2020
- Two-Shot Spatially-Varying BRDF and Shape EstimationMark Boss, Varun Jampani, Kihwan Kim, Hendrik P. A. Lensch et al.CVPR 2020
Related papers
- MVInverse: Feed-forward Multiview Inverse Rendering in SecondsXiangzuo Wu, Chengwei Ren, Jun Zhou, Xiu Li et al.CVPR 2026
- Weakly-supervised Single-view Image RelightingRenjiao Yi, Chenyang Zhu, Kai XuCVPR 2023
- Differentiable Inverse Rendering with Interpretable Basis BRDFsHoon-Gyu Chung, Seokjun Choi, Seung-Hwan BaekCVPR 2025
- LIRM: Large Inverse Rendering Model for Progressive Reconstruction of Shape, Materials and View-dependent Radiance FieldsZhengqin Li, Dilin Wang, Ka Chen, Zhaoyang Lv et al.CVPR 2025
- S3-NeRF: Neural Reflectance Field from Shading and Shadow under a Single ViewpointWenqi Yang, Guanying Chen, Chaofeng Chen, Zhenfang Chen et al.NeurIPS 2022 · 48 citations
