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 …

Neural volume rendering became increasingly popular recently due to its success in
synthesizing novel views of a scene from a sparse set of input images. So far, the geometry …

We present Neural Feature Fusion Fields (N3F),\a method that improves dense 2D image
feature extractors when the latter are applied to the analysis of multiple images …

Abstract We present Non-Rigid Neural Radiance Fields (NR-NeRF), a reconstruction and
novel view synthesis approach for general non-rigid dynamic scenes. Our approach takes …

Abstract Neural Radiance Field (NeRF) has gained considerable attention recently for 3D
scene reconstruction and novel view synthesis due to its remarkable synthesis quality …

Neural Radiance Fields (NeRFs) have recently emerged as a powerful paradigm for the
representation of natural, complex 3D scenes. Neural Radiance Fields (NeRFs) represent …

Neural radiance fields (NeRF) and its subsequent variants have led to remarkable progress
in neural rendering. While most of recent neural rendering works focus on objects and small …

We present Loc-NeRF, a real-time vision-based robot localization approach that combines
Monte Carlo localization and Neural Radiance Fields (NeRF). Our system uses a pre-trained …

In many recent works, multi-layer perceptions (MLPs) have been shown to be suitable for
modeling complex spatially-varying functions including images and 3D scenes. Although the …

Abstract Recently, Neural Radiance Field (NeRF) has shown great success in rendering
novel-view images of a given scene by learning an implicit representation with only posed …