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.

翻译：用显式体素网格表示神经辐射场（NeRF）是改进NeRF的一个有前景的方向。然而，由于巨大的内存消耗，显式体素网格表示在存储和传输方面效率不高。当前压缩显式体素网格的方法主要继承自神经网络压缩方法（如剪枝和量化），这些方法未能充分利用体素间的空间相关性。受蓬勃发展的数字图像压缩技术启发，本文提出SPC-NeRF——一种将空间预测编码应用于显式体素网格压缩的新颖框架。该框架能高效去除空间冗余以实现更优的压缩性能。此外，我们对比特率进行建模并设计了新型损失函数，可联合优化压缩比与失真以实现更高的编码效率。大量实验表明，在多个代表性测试数据集上，我们的方法相比最先进的VQRF方法可节省32%的比特率，且训练时间相当。