Embedding polygonal mesh assets within photorealistic Neural Radience Fields (NeRF)
volumes, such that they can be rendered and their dynamics simulated in a physically …

We present a method for reducing errors---variance and bias---in physically based
differentiable rendering (PBDR). Typical applications of PBDR repeatedly render a scene as …

We develop a theory for the representation of opaque solids as volumes. Starting from a
stochastic representation of opaque solids as random indicator functions we prove the …

Recovering the physical attributes of an object's appearance from its images captured under
an unknown illumination is challenging yet essential for photo-realistic rendering. Recent …

Physics-based differentiable rendering is becoming increasingly crucial for tasks in inverse
rendering and machine learning pipelines. To address discontinuities caused by geometric …

We propose an efficient method for differentiable rendering of parametric surfaces and
curves, which enables their use in inverse graphics problems. Our central observation is that …

Neural Radiance Fields (NeRF) has demonstrated its ability to generate high-quality
synthesized views. Nonetheless, due to its slow inference speed, there is a need to explore …

The efficiency of inverse optimization in physically based differentiable rendering heavily
depends on the variance of Monte Carlo estimation. Despite recent advancements …

Differentiable rendering of translucent objects with respect to their shapes has been a long‐
standing problem. State‐of‐the‐art methods require detecting object silhouettes or …

We propose a set of techniques to efficiently importance sample the derivatives of a wide
range of Bidirectional Reflectance Distribution Function (BRDF) models. In differentiable …