Learning-based Inverse Rendering of Complex Indoor Scenes with Differentiable Monte Carlo Raytracing. (arXiv:2211.03017v2 [cs.CV] UPDATED)

Indoor scenes typically exhibit complex, spatially-varying appearance from
global illumination, making inverse rendering a challenging ill-posed problem.
This work presents an end-to-end, learning-based inverse rendering framework
incorporating differentiable Monte Carlo raytracing with importance sampling.
The framework takes a single image as input to jointly recover the underlying
geometry, spatially-varying lighting, and photorealistic materials.
Specifically, we introduce a physically-based differentiable rendering layer
with screen-space ray tracing, resulting in more realistic specular reflections
that match the input photo. In addition, we create …

arxiv inverse rendering rendering