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.

翻译：本文提出二元辐射场（Binary Radiance Fields, BiRF），一种采用二元特征编码的高存储效率辐射场表示方法，通过将局部特征编码为+1或-1的二元参数格式。这种二值化策略使我们能够以高度紧凑的特征编码表示特征网格，并显著降低存储空间。此外，我们设计的2D-3D混合特征网格通过三维网格捕捉主要分量、二维网格捕获细节特征，进一步增强了特征编码的紧凑性。实验表明，二元辐射场表示在更低存储开销下，成功超越了现有最优（SOTA）高效辐射场模型的重建性能。具体而言，本模型在静态场景重建中取得了显著成果：Synthetic-NeRF场景下PSNR达31.53 dB，Synthetic-NSVF场景达34.26 dB，Tanks and Temples场景达28.02 dB，而对应存储空间仅需0.7 MB、0.8 MB和0.8 MB。我们期望所提出的二元辐射场表示能够消除存储瓶颈，推动辐射场技术的更广泛应用。