Neural radiance fields have achieved remarkable performance in modeling the appearance
of 3D scenes. However existing approaches still struggle with the view-dependent …

This paper presents a method, namely NeuS-PIR, for recovering relightable neural surfaces
using pre-integrated rendering from multi-view images or video. Unlike methods based on …

Existing NeRF-based inverse rendering methods suppose that scenes are exclusively
illuminated by distant light sources neglecting the potential influence of emissive sources …

This paper introduces a versatile multi-view inverse rendering framework with near-and far-
field light sources. Tackling the fundamental challenge of inherent ambiguity in inverse …

An increasingly common approach for creating photo-realistic digital avatars is through the
use of volumetric neural fields. The original neural radiance field (NeRF) allowed for …

Online dense mapping of urban scenes is of paramount importance for scene understanding
of autonomous navigation. Traditional online dense mapping methods fuse sensor …

The generation of global illumination in real time has been a long-standing challenge in the
graphics community, particularly in dynamic scenes with complex illumination. Recent …

We present a method for capturing the BSSRDF (bidirectional scattering‐surface reflectance
distribution function) of arbitrary geometry with a neural network. We demonstrate how a …

Relighting radiance fields is severely underconstrained for multi‐view data, which is most
often captured under a single illumination condition; It is especially hard for full scenes …

The insertion of objects into a scene and relighting are commonly utilized applications in
augmented reality (AR). Previous methods focused on inserting virtual objects using CAD …