Abstract Neural Radiance Field (NeRF) has emerged as a powerful paradigm for scene
representation, offering high-fidelity renderings and reconstructions from a set of sparse and …

Abstract We present Neural Fields for LiDAR (NFL), a method to optimise a neural field
scene representation from LiDAR measurements, with the goal of synthesizing realistic …

Abstract Neural Radiance Fields (NeRF) achieves impressive ren-dering performance by
learning volumetric 3D representation from several images of different views. However, it is …

Event cameras offer many advantages over standard cameras due to their distinctive
principle of operation: low power, low latency, high temporal resolution and high dynamic …

Abstract Modeling Neural Radiance Fields for fast-moving deformable objects from visual
data alone is a challenging problem. A major issue arises due to the high deformation and …

Abstract Neural Radiance Fields (NeRFs) have shown great potential in novel view
synthesis. However they struggle to render sharp images when the data used for training is …

Abstract We present Ev-NeRF, a Neural Radiance Field derived from event data. While
event cameras can measure subtle brightness changes in high frame rates, the …

Estimating neural radiance fields (NeRFs) from “ideal” images has been extensively studied
in the computer vision community. Most approaches assume optimal illumination and slow …

We present EvDNeRF, a pipeline for generating event data and training an event-based
dynamic NeRF, for the purpose of faithfully reconstructing eventstreams on scenes with rigid …