  While existing Neural Radiance Fields (NeRFs) for dynamic scenes are offline
methods with an emphasis on visual fidelity, our paper addresses the online use
case that prioritises real-time adaptability. We present ParticleNeRF, a new
approach that dynamically adapts to changes in the scene geometry by learning
an up-to-date representation online, every 200ms. ParticleNeRF achieves this
using a novel particle-based parametric encoding. We couple features to
particles in space and backpropagate the photometric reconstruction loss into
the particles' position gradients, which are then interpreted as velocity
vectors. Governed by a lightweight physics system to handle collisions, this
lets the features move freely with the changing scene geometry. We demonstrate
ParticleNeRF on various dynamic scenes containing translating, rotating,
articulated, and deformable objects. ParticleNeRF is the first online dynamic
NeRF and achieves fast adaptability with better visual fidelity than
brute-force online InstantNGP and other baseline approaches on dynamic scenes
with online constraints. Videos of our system can be found at our project
website https://sites.google.com/view/particlenerf.
