Plug-and-Play Acceleration of Occupancy Grid-based NeRF Rendering using
  VDB Grid and Hierarchical Ray Traversal
Yoshio Kato Shuhei Tarashima
Transmittance estimators such as Occupancy Grid (OG) can accelerate the
training and rendering of Neural Radiance Field (NeRF) by predicting important
samples that contributes much to the generated image. However, OG manages
occupied regions in the form of the dense binary grid, in which there are many
blocks with the same values that cause redundant examination of voxels'
emptiness in ray-tracing. In our work, we introduce two techniques to improve
the efficiency of ray-tracing in trained OG without fine-tuning. First, we
replace the dense grids with VDB grids to reduce the spatial redundancy.
Second, we use hierarchical digital differential analyzer (HDDA) to efficiently
trace voxels in the VDB grids. Our experiments on NeRF-Synthetic and Mip-NeRF
360 datasets show that our proposed method successfully accelerates rendering
NeRF-Synthetic dataset by 12% in average and Mip-NeRF 360 dataset by 4% in
average, compared to a fast implementation of OG, NerfAcc, without losing the
quality of rendered images.