Solar design can take many different forms across disciplines with different methodologies
and goals, ranging from acquiring architectural visual effects to assessing illumination for …

Neural Radiance Fields (NeRF) are a rapidly growing area of research with wide-ranging
applications in computer vision, graphics, robotics, and more. In order to streamline the …

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 …

Translucency is an optical and a perceptual phenomenon that characterizes subsurface light
transport through objects and materials. Translucency as an optical property of a material …

Modern rendering systems are confronted with a dauntingly large and growing set of
requirements: in their pursuit of realism, physically based techniques must increasingly …

We present a benchmark for image-based 3D reconstruction. The benchmark sequences
were acquired outside the lab, in realistic conditions. Ground-truth data was captured using …

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 …

We introduce a data-driven approach to complete partial 3D shapes through a combination
of volumetric deep neural networks and 3D shape synthesis. From a partially-scanned input …

We address the problem of finding realistic geometric corrections to a foreground object
such that it appears natural when composited into a background image. To achieve this, we …

The success of deep learning in computer vision is based on the availability of large
annotated datasets. To lower the need for hand labeled images, virtually rendered 3D …