PhySG: Inverse Rendering With Spherical Gaussians for Physics-Based Material Editing and Relighting
Kai Zhang, Fujun Luan, Qianqian Wang, Kavita Bala, Noah Snavely
Abstract
We present PhySG, an end-to-end inverse rendering pipeline that includes a fully differentiable renderer and can reconstruct geometry, materials, and illumination from scratch from a set of RGB input images. Our framework represents specular BRDFs and environmental illumination using mixtures of spherical Gaussians, and represents geometry as a signed distance function parameterized as a Multi-Layer Perceptron. The use of spherical Gaussians allows us to efficiently solve for approximate light transport, and our method works on scenes with challenging non-Lambertian reflectance captured under natural, static illumination. We demonstrate, with both synthetic and real data, that our reconstructions not only enable rendering of novel viewpoints, but also physics-based appearance editing of materials and illumination.
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.
Your agent calls
Luneget_paper_fulltext
Free to start. No credit card required.
Terminal
Install the CLIlune papers fulltext d949a771-5e6f-4b71-beb7-227f95660e7fCited by top-tier papers170
- NeuS: Learning Neural Implicit Surfaces by Volume Rendering for Multi-view ReconstructionPeng Wang, Lingjie Liu, Yuan Liu, Christian Theobalt et al.NeurIPS 2021 · 2,500 citations
- GET3D: A Generative Model of High Quality 3D Textured Shapes Learned from ImagesJun Gao, Tianchang Shen, Zian Wang, Wenzheng Chen et al.NeurIPS 2022 · 661 citations
- NeRD: Neural Reflectance Decomposition from Image CollectionsMark Boss, Raphael Braun, Varun Jampani, Jonathan T. Barron et al.ICCV 2021 · 608 citations
- Ref-NeRF: Structured View-Dependent Appearance for Neural Radiance FieldsDor Verbin, Peter Hedman, Ben Mildenhall, Todd E. Zickler et al.CVPR 2022 · 477 citations
- Extracting Triangular 3D Models, Materials, and Lighting From ImagesJacob Munkberg, Wenzheng Chen, Jon Hasselgren, Alex Evans et al.CVPR 2022 · 306 citations
Builds on9
- Fourier Features Let Networks Learn High Frequency Functions in Low Dimensional DomainsMatthew Tancik, Pratul P. Srinivasan, Ben Mildenhall, Sara Fridovich-Keil et al.NeurIPS 2020 · 4,036 citations
- Implicit Geometric Regularization for Learning ShapesAmos Gropp, Lior Yariv, Niv Haim, Matan Atzmon et al.ICML 2020 · 1,001 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
- Seeing the World in a Bag of ChipsJeong Joon Park, Aleksander Holynski, Steven M. SeitzCVPR 2020
- Deep 3D Capture: Geometry and Reflectance From Sparse Multi-View ImagesSai Bi, Zexiang Xu, Kalyan Sunkavalli, David J. Kriegman et al.CVPR 2020
Related papers
- NeFII: Inverse Rendering for Reflectance Decomposition with Near-Field Indirect IlluminationHaoqian Wu, Zhipeng Hu, Lincheng Li, Yongqiang Zhang et al.CVPR 2023
- Factored-NeuS: Reconstructing Surfaces, Illumination, and Materials of Possibly Glossy ObjectsYue Fan, Ningjing Fan, Ivan Skorokhodov, Oleg Voynov et al.CVPR 2025
- 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
- PBR-NeRF: Inverse Rendering with Physics-Based Neural FieldsSean Wu, Shamik Basu, Tim Broedermann, Luc Van Gool et al.CVPR 2025
- ENVIDR: Implicit Differentiable Renderer with Neural Environment LightingRuofan Liang, Huiting Chen, Chunlin Li, Fan Chen et al.ICCV 2023 · 72 citations
