In this paper, we propose binary radiance fields (BiRF), a storage-efficient
radiance field representation employing binary feature encoding that encodes
local features using binary encoding parameters in a format of either $+1$ or
$-1$. This binarization strategy lets us represent the feature grid with highly
compact feature encoding and a dramatic reduction in storage size. Furthermore,
our 2D-3D hybrid feature grid design enhances the compactness of feature
encoding as the 3D grid includes main components while 2D grids capture
details. In our experiments, binary radiance field representation successfully
outperforms the reconstruction performance of state-of-the-art (SOTA) efficient
radiance field models with lower storage allocation. In particular, our model
achieves impressive results in static scene reconstruction, with a PSNR of
31.53 dB for Synthetic-NeRF scenes, 34.26 dB for Synthetic-NSVF scenes, 28.02
dB for Tanks and Temples scenes while only utilizing 0.7 MB, 0.8 MB, and 0.8 MB
of storage space, respectively. We hope the proposed binary radiance field
representation will make radiance fields more accessible without a storage
bottleneck.

本文提出了一种存储效率高、能够编码局部特征的二进制辐射场表示，其二进制化策略让我们能够使用高度紧凑的特征编码来表示功能网格，并显著减小存储器的大小。在静态场景的重建性能上，我们的模型可以达到令人印象深刻的结果。