Quantum mechanics

Radiance

Gaussian

Image-based lighting

Image-based modeling and rendering

Real-time rendering

Computer graphics (images)

Global illumination

Computer science

Geology

Rendering (computer graphics)

Physics

Optics

RGB color model

Artificial intelligence

Remote sensing

Ground truth

3D rendering

Computer vision

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.

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