We address the problem of recovering the shape and spatially-varying reflectance of an
object from multi-view images (and their camera poses) of an object illuminated by one …

Gradient-based methods are becoming increasingly important for computer graphics,
machine learning, and computer vision. The ability to compute gradients is crucial to …

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 …

Reconstructing shape and reflectance properties from images is a highly under-constrained
problem, and has previously been addressed by using specialized hardware to capture …

Texture, highlights, and shading are some of many visual cues that allow humans to
perceive material appearance in single pictures. Yet, recovering spatially-varying bi …

Estimating the surface reflectance properties of a spatially-varying material is a challenging
problem. Methods based on inverse rendering (eg,[Dong et al. 2014; Hui et al. 2017]) can …

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 …

This paper surveys the state-of-the-art of research in patch-based synthesis. Patch-based
methods synthesize output images by copying small regions from exemplar imagery. This …

Reconstructing the shape and appearance of real‐world objects using measured 2D images
has been a long‐standing inverse rendering problem. In this paper, we introduce a new …

We address the problem of reconstructing spatially-varying BRDFs from a small set of image
measurements. This is a fundamentally under-constrained problem, and previous work has …