SPC-NeRF: Spatial Predictive Compression for Voxel Based Radiance Field
Zetian Song Wenhong Duan Yuhuai Zhang Shiqi Wang Siwei Ma Wen Gao
Representing the Neural Radiance Field (NeRF) with the explicit voxel grid
(EVG) is a promising direction for improving NeRFs. However, the EVG
representation is not efficient for storage and transmission because of the
terrific memory cost. Current methods for compressing EVG mainly inherit the
methods designed for neural network compression, such as pruning and
quantization, which do not take full advantage of the spatial correlation of
voxels. Inspired by prosperous digital image compression techniques, this paper
proposes SPC-NeRF, a novel framework applying spatial predictive coding in EVG
compression. The proposed framework can remove spatial redundancy efficiently
for better compression performance.Moreover, we model the bitrate and design a
novel form of the loss function, where we can jointly optimize compression
ratio and distortion to achieve higher coding efficiency. Extensive experiments
demonstrate that our method can achieve 32% bit saving compared to the
state-of-the-art method VQRF on multiple representative test datasets, with
comparable training time.