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.

该论文提出了一种新颖的框架 SPC-NeRF，应用空间预测编码在显式体素网格（EVG）压缩中，以实现更好的压缩性能和 32％的比特节省。

SPC-NeRF：基于体素辐射场空间预测压缩

SPC-NeRF: Spatial Predictive Compression for Voxel Based Radiance Field

NeRFs have revolutionized the world of per-scene radiance field
reconstruction because of their intrinsic compactness. One of the main
limitations of NeRFs is their slow rendering speed, both at training and
inference time. Recent research focuses on the optimization of an explicit
voxel grid (EVG) that represents the scene, which can be paired with neural
networks to learn radiance fields. This approach significantly enhances the
speed both at train and inference time, but at the cost of large memory
occupation. In this work we propose Re:NeRF, an approach that specifically
targets EVG-NeRFs compressibility, aiming to reduce memory storage of NeRF
models while maintaining comparable performance. We benchmark our approach with
three different EVG-NeRF architectures on four popular benchmarks, showing
Re:NeRF's broad usability and effectiveness.

本文提出了 Re:NeRF 方法，用于在维持性能的同时降低 NeRF 模型的内存存储。与三种不同的 EXG-NeRF 体系结构在四个流行的基准测试中进行基准测试，显示了 Re:NeRF 的广泛适用性和有效性。