Explicit feature-grid based NeRF models have shown promising results in terms
of rendering quality and significant speed-up in training. However, these
methods often require a significant amount of data to represent a single scene
or object. In this work, we present a compression model that aims to minimize
the entropy in the frequency domain in order to effectively reduce the data
size. First, we propose using the discrete cosine transform (DCT) on the
tensorial radiance fields to compress the feature-grid. This feature-grid is
transformed into coefficients, which are then quantized and entropy encoded,
following a similar approach to the traditional video coding pipeline.
Furthermore, to achieve a higher level of sparsity, we propose using an entropy
parameterization technique for the frequency domain, specifically for DCT
coefficients of the feature-grid. Since the transformed coefficients are
optimized during the training phase, the proposed model does not require any
fine-tuning or additional information. Our model only requires a lightweight
compression pipeline for encoding and decoding, making it easier to apply
volumetric radiance field methods for real-world applications. Experimental
results demonstrate that our proposed frequency domain entropy model can
achieve superior compression performance across various datasets. The source
code will be made publicly available.

提出了一种基于频域熵建模的压缩模型，通过离散余弦变换（DCT）对特征网格进行压缩，进一步使用熵参数化技术提高稀疏性，实现轻量级压缩编解码，从而使得体积辐射场方法更易于在实际应用中使用。实验结果表明，所提出的频域熵模型在多个数据集上达到了卓越的压缩性能。