Decomposing a scene into its shape, reflectance, and illumination is a challenging but
important problem in computer vision and graphics. This problem is inherently more …

Decomposing a scene into its shape, reflectance and illumination is a fundamental problem
in computer vision and graphics. Neural approaches such as NeRF have achieved …

Abstract We introduce Amazon Berkeley Objects (ABO), a new large-scale dataset designed
to help bridge the gap between real and virtual 3D worlds. ABO contains product catalog …

Inverse rendering of an object under entirely unknown capture conditions is a fundamental
challenge in computer vision and graphics. Neural approaches such as NeRF have …

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 …

Appearance modeling is an essential task in computer graphics for capturing and
reproducing rich appearance of real world materials under different lighting and viewing …

We present a novel method for efficient acquisition of shape and spatially varying
reflectance of 3D objects using polarization cues. Unlike previous works that have exploited …

This paper addresses the task of estimating spatially-varying reflectance (ie, SVBRDF) from
a single, casually captured image. Central to our method is a highlight-aware (HA) …

3D symmetry detection is a fundamental problem in computer vision and graphics. Most
prior works detect symmetry when the object model is fully known, few studies symmetry …

Empowered by deep learning, recent methods for material capture can estimate a spatially‐
varying reflectance from a single photograph. Such lightweight capture is in stark contrast …