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。

基于即时 NGP 的 NeRF 可以压缩到什么程度？

How Far Can We Compress Instant-NGP-Based NeRF?

Levin Tree Search (LTS) is a search algorithm that makes use of a policy (a
probability distribution over actions) and comes with a theoretical guarantee
on the number of expansions before reaching a goal node, depending on the
quality of the policy. This guarantee can be used as a loss function, which we
call the LTS loss, to optimize neural networks representing the policy
(LTS+NN). In this work we show that the neural network can be substituted with
parameterized context models originating from the online compression literature
(LTS+CM). We show that the LTS loss is convex under this new model, which
allows for using standard convex optimization tools, and obtain convergence
guarantees to the optimal parameters in an online setting for a given set of
solution trajectories -- guarantees that cannot be provided for neural
networks. The new LTS+CM algorithm compares favorably against LTS+NN on several
benchmarks: Sokoban (Boxoban), The Witness, and the 24-Sliding Tile puzzle
(STP). The difference is particularly large on STP, where LTS+NN fails to solve
most of the test instances while LTS+CM solves each test instance in a fraction
of a second. Furthermore, we show that LTS+CM is able to learn a policy that
solves the Rubik's cube in only a few hundred expansions, which considerably
improves upon previous machine learning techniques.

本文介绍了一种基于 Levin Tree Search (LTS) 算法的 LTS+CM 算法，使用参数化的上下文模型并在具有凸保证的基础上优化策略。此算法在 Sokoban，The Witness 和 24 滑动方块 (STP) 等多个问题上表现优异，能够解决魔方等问题，且相对于之前的机器学习技术有很大的进步。