Abstract
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.
Abstract (translated)
使用明确体素网格（EVG）表示神经元辐射场（NeRF）是一个改进NeRFs的有前途的方向。然而，由于出色的内存成本，EVG表示并不高效地进行存储和传输。目前用于压缩EVG的方法主要继承了为神经网络压缩设计的算法，如剪枝和量化，这些方法没有充分利用体素之间的空间关联。受到繁荣的数字图像压缩技术的启发，本文提出了SPC-NeRF，一种在EVG压缩中应用空间预测编码的新框架。与现有的方法相比，所提出的框架可以有效地消除空间冗余，从而提高压缩性能。此外，我们建模了带宽和设计了一种新的损失函数，使得我们能够共同优化压缩比和失真，以实现更高的编码效率。大量实验证明，与最先进的VQRF方法相比，我们的方法可以在多个代表性测试数据集上实现32%的带宽节省，具有与训练时间相当的可比训练时间。
URL
https://arxiv.org/abs/2402.16366
