Shape, Light, and Material Decomposition from Images using Monte Carlo Rendering and DenoisingDownload PDF

Published: 31 Oct 2022, 18:00, Last Modified: 12 Oct 2022, 16:34NeurIPS 2022 AcceptReaders: Everyone
Keywords: Applications, Optimization, Deep Learning
TL;DR: Reconstructing 3D meshes, materials and lighting from images, using inverse rendering with a physically-based shading model.
Abstract: Recent advances in differentiable rendering have enabled high-quality reconstruction of 3D scenes from multi-view images. Most methods rely on simple rendering algorithms: pre-filtered direct lighting or learned representations of irradiance. We show that a more realistic shading model, incorporating ray tracing and Monte Carlo integration, substantially improves decomposition into shape, materials & lighting. Unfortunately, Monte Carlo integration provides estimates with significant noise, even at large sample counts, which makes gradient-based inverse rendering very challenging. To address this, we incorporate multiple importance sampling and denoising in a novel inverse rendering pipeline. This improves convergence and enables gradient-based optimization at low sample counts. We present an efficient method to jointly reconstruct geometry (explicit triangle meshes), materials, and lighting, which substantially improves material and light separation compared to previous work. We argue that denoising can become an integral part of high quality inverse rendering pipelines.
Supplementary Material: zip
11 Replies