We present TensoRF, a novel approach to model and reconstruct radiance fields. Unlike
NeRF that purely uses MLPs, we model the radiance field of a scene as a 4D tensor, which …

We present an end-to-end inverse rendering pipeline that includes a fully differentiable
renderer, and can reconstruct geometry, materials, and illumination from scratch from a set …

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 …

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

Reconstructing the shape and spatially varying surface appearances of a physical-world
object as well as its surrounding illumination based on 2D images (eg, photographs) of the …

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

Capturing spatially-varying bidirectional reflectance distribution functions (SVBRDFs) of 3D
objects with just a single, hand-held camera (such as an off-the-shelf smartphone or a DSLR …

The goal of light transport acquisition is to take images from a sparse set of lighting and
viewing directions, and combine them to enable arbitrary relighting with changing view …

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) …