Compression-Aware Tuning for Compressing Volumetric Radiance Fields

Reconstructing neural radiance fields with explicit volumetric grids can significantly improve training and inference efficiency. However, these methods store features on voxels, inevitably increasing the cost of storage and transmission for scenes. To solve this limitation, some recent works are inspired by model compression methods, reducing storage costs through pruning and quantization. In this paper, we propose a simple and effective framework called compression-aware vector quantized radiance fields (CA-VQRF), aiming to improve the view synthesis performance of the compressed grid while ensuring the compression ratio. We discover the shortcomings of the existing work and propose corresponding solutions. Specifically, we introduce a pruning-aware tuning strategy to shield the impact of the pruned-off voxels in the joint finetuning stage. In addition, a noise-aware tuning strategy is proposed to further compensate for the performance loss caused by vector quantization. Extensive experiments demonstrate that compared with the state-of-the-art compression method, our CA-VQRF can achieve better view synthesis results at the same compression ratio.