HollowNeRF: Pruning Hashgrid-Based NeRFs with Trainable Collision
  Mitigation
Xiufeng Xie Riccardo Gherardi Zhihong Pan Stephen Huang
Neural radiance fields (NeRF) have garnered significant attention, with
recent works such as Instant-NGP accelerating NeRF training and evaluation
through a combination of hashgrid-based positional encoding and neural
networks. However, effectively leveraging the spatial sparsity of 3D scenes
remains a challenge. To cull away unnecessary regions of the feature grid,
existing solutions rely on prior knowledge of object shape or periodically
estimate object shape during training by repeated model evaluations, which are
costly and wasteful.
  To address this issue, we propose HollowNeRF, a novel compression solution
for hashgrid-based NeRF which automatically sparsifies the feature grid during
the training phase. Instead of directly compressing dense features, HollowNeRF
trains a coarse 3D saliency mask that guides efficient feature pruning, and
employs an alternating direction method of multipliers (ADMM) pruner to
sparsify the 3D saliency mask during training. By exploiting the sparsity in
the 3D scene to redistribute hash collisions, HollowNeRF improves rendering
quality while using a fraction of the parameters of comparable state-of-the-art
solutions, leading to a better cost-accuracy trade-off. Our method delivers
comparable rendering quality to Instant-NGP, while utilizing just 31% of the
parameters. In addition, our solution can achieve a PSNR accuracy gain of up to
1dB using only 56% of the parameters.