ParticleNeRF: Particle Based Encoding for Online Neural Radiance Fields in Dynamic Scenes

Neural Radiance Fields (NeRFs) are coordinate-based implicit representations of 3D scenes that use a differentiable rendering procedure to learn a representation of an environment from images. This paper extends NeRFs to handle dynamic scenes in an online fashion. We do so by introducing a particle-based parametric encoding, which allows the intermediate NeRF features -- now coupled to particles in space -- to be moved with the dynamic geometry. We backpropagate the NeRF's photometric reconstruction loss into the position of the particles in addition to the features they are associated with. The position gradients are interpreted as particle velocities and integrated into positions using a position-based dynamics (PBS) physics system. Introducing PBS into the NeRF formulation allows us to add collision constraints to the particle motion and creates future opportunities to add other movement priors into the system such as rigid and deformable body constraints. We show that by allowing the features to move in space, we incrementally adapt the NeRF to the changing scene.