Recent advances in machine learning have led to increased interest in solving visual
computing problems using methods that employ coordinate‐based neural networks. These …

Research on neural radiance fields (NeRFs) for novel view generation is exploding with new
models and extensions. However, a question that remains unanswered is what happens in …

We present Neural Reflectance Fields, a novel deep scene representation that encodes
volume density, normal and reflectance properties at any 3D point in a scene using a fully …

Urban areas are a high-stake target of climate change mitigation and adaptation measures.
To understand, predict, and improve the energy performance of cities, the scientific …

We present a method for composing photorealistic scenes from captured images of objects.
Our work builds upon neural radiance fields (NeRFs), which implicitly model the volumetric …

We present a deep learning approach to reconstruct scene appearance from unstructured
images captured under collocated point lighting. At the heart of Deep Reflectance Volumes …

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

Physics-based differentiable rendering is the task of estimating the derivatives of radiometric
measures with respect to scene parameters. The ability to compute these derivatives is …

Physics-based differentiable rendering---which focuses on estimating derivatives of
radiometric detector responses with respect to arbitrary scene parameters---has a diverse …

We present a novel Monte Carlo-based fluid simulation approach capable of pointwise and
stochastic estimation of fluid motion. Drawing on the Feynman-Kac representation of the …