In recent years, Neural Radiance Field (NeRF) has demonstrated remarkable
capabilities in representing 3D scenes. To expedite the rendering process,
learnable explicit representations have been introduced for combination with
implicit NeRF representation, which however results in a large storage space
requirement. In this paper, we introduce the Context-based NeRF Compression
(CNC) framework, which leverages highly efficient context models to provide a
storage-friendly NeRF representation. Specifically, we excavate both level-wise
and dimension-wise context dependencies to enable probability prediction for
information entropy reduction. Additionally, we exploit hash collision and
occupancy grids as strong prior knowledge for better context modeling. To the
best of our knowledge, we are the first to construct and exploit context models
for NeRF compression. We achieve a size reduction of 100$\times$ and 70$\times$
with improved fidelity against the baseline Instant-NGP on Synthesic-NeRF and
Tanks and Temples datasets, respectively. Additionally, we attain 86.7\% and
82.3\% storage size reduction against the SOTA NeRF compression method BiRF.
Our code is available here: this https URL

我们介绍了一种基于上下文的 NeRF 压缩 (CNC) 框架，利用高效的上下文模型提供储存友好的 NeRF 表示。我们通过挖掘层级和维度上的上下文依赖以减少信息熵，同时利用哈希碰撞和占用栅格作为强先验知识进行更好的上下文建模，实现了对 Synthesic-NeRF 和 Tanks and Temples 数据集上基准 Instant-NGP 的体积降低 100 倍和 70 倍，对 SOTA NeRF 压缩方法 BiRF 实现了 86.7% 和 82.3% 的存储空间减少。