Inverse Rendering for Complex Indoor Scenes: Shape, Spatially-Varying Lighting and SVBRDF from a Single Image

Inverse Rendering for Complex Indoor Scenes:
Shape, Spatially-Varying Lighting and SVBRDF from a Single Image

CVPR 2020 (Oral presentation)

Zhengqin Li1 Mohammad Shafiei1 Ravi Ramamoorthi1
Kalyan Sunkavalli2 Manmohan Chandraker1
1University of California, San Diego 2Adobe


We propose a deep inverse rendering framework for indoor scenes. From a single RGB image of an arbitrary indoor scene, we obtain a complete scene reconstruction, estimating shape, spatially-varying lighting, and spatially-varying, non-Lambertian surface reflectance. Our novel inverse rendering network incorporates physical insights -- including a spatially-varying spherical Gaussian lighting representation, a differentiable rendering layer to model scene appearance, a cascade structure to iteratively refine the predictions and a bilateral solver for refinement -- allowing us to jointly reason about shape, lighting, and reflectance. Since no existing dataset provides ground truth high quality spatially-varying material and spatially-varying lighting, we propose novel methods to map complex materials to existing indoor scene datasets and a new physically-based GPU renderer to create a large-scale, photorealistic indoor dataset. Experiments show that our framework outperforms previous methods and enables various novel applications like photorealistic object insertion and material editing.

Augment synthetic indoor dataset with complex SVBRDF

Photorealistic real scene editing application 1: object insertion

Photorealistic real scene editing application 2: material editing


Paper Supplementary
20 Objects Inseretion
Dataset Models
Tileable Texture Object Insertion Renderer
Spherical Gaussian


title={Inverse rendering for complex indoor scenes: Shape, spatially-varying lighting and svbrdf from a single image},
author={Li, Zhengqin and Shafiei, Mohammad and Ramamoorthi, Ravi and Sunkavalli, Kalyan and Chandraker, Manmohan},
booktitle={Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition},