  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-connected neural network. We combine this representation
with a physically-based differentiable ray marching framework that can render
images from a neural reflectance field under any viewpoint and light. We
demonstrate that neural reflectance fields can be estimated from images
captured with a simple collocated camera-light setup, and accurately model the
appearance of real-world scenes with complex geometry and reflectance. Once
estimated, they can be used to render photo-realistic images under novel
viewpoint and (non-collocated) lighting conditions and accurately reproduce
challenging effects like specularities, shadows and occlusions. This allows us
to perform high-quality view synthesis and relighting that is significantly
better than previous methods. We also demonstrate that we can compose the
estimated neural reflectance field of a real scene with traditional scene
models and render them using standard Monte Carlo rendering engines. Our work
thus enables a complete pipeline from high-quality and practical appearance
acquisition to 3D scene composition and rendering.
