  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 substantially 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.
