Neural radiance fields (NeRFs) refer to a suit of deep neural networks that are used to learn
and represent objects or scenes. Generally speaking, NeRFs have five main characters …

Implicit neural representation has paved the way for new approaches to dynamic scene
reconstruction. Nonetheless cutting-edge dynamic neural rendering methods rely heavily on …

Implicit neural representations (INRs) have recently advanced numerous vision-related
areas. INR performance depends strongly on the choice of activation function employed in …

Neural radiance fields (NeRF) have shown great success in modeling 3D scenes and
synthesizing novel-view images. However, most previous NeRF methods take much time to …

Abstract Neural Radiance Fields (NeRF) is a technique for high quality novel view synthesis
from a collection of posed input images. Like most view synthesis methods, NeRF uses …

Recent advances in machine learning have led to increased interest in solving visual
computing problems using methods that employ coordinate‐based neural networks. These …

A commonly observed failure mode of Neural Radiance Field (NeRF) is fitting incorrect
geometries when given an insufficient number of input views. One potential reason is that …

Synthesizing photo‐realistic images and videos is at the heart of computer graphics and has
been the focus of decades of research. Traditionally, synthetic images of a scene are …

Volumetric scene representations enable photorealistic view synthesis for static scenes and
form the basis of several existing 6-DoF video techniques. However, the volume rendering …

Implicit neural representations such as Neural Radiance Field (NeRF) have focused mainly
on modeling static objects captured under multi-view settings where real-time rendering can …