Rate-aware Compression for NeRF-based Volumetric Video

TL;DR

This paper introduces a novel rate-aware compression framework for NeRF-based volumetric video that directly learns compact representations during training, significantly reducing storage size while maintaining quality.

Contribution

It proposes a rate-aware training method with an implicit entropy model, adaptive quantization, and rate-distortion optimization for NeRF representations, achieving state-of-the-art compression performance.

Findings

Reduces storage size by approximately 80% on HumanRF dataset.

Achieves state-of-the-art rate-distortion performance.

Maintains marginal distortion with significant compression gains.

Abstract

The neural radiance fields (NeRF) have advanced the development of 3D volumetric video technology, but the large data volumes they involve pose significant challenges for storage and transmission. To address these problems, the existing solutions typically compress these NeRF representations after the training stage, leading to a separation between representation training and compression. In this paper, we try to directly learn a compact NeRF representation for volumetric video in the training stage based on the proposed rate-aware compression framework. Specifically, for volumetric video, we use a simple yet effective modeling strategy to reduce temporal redundancy for the NeRF representation. Then, during the training phase, an implicit entropy model is utilized to estimate the bitrate of the NeRF representation. This entropy model is then encoded into the bitstream to assist in the decoding of the NeRF representation. This approach enables precise bitrate estimation, thereby leading to a compact NeRF representation. Furthermore, we propose an adaptive quantization strategy and learn the optimal quantization step for the NeRF representations. Finally, the NeRF representation can be optimized by using the rate-distortion trade-off. Our proposed compression framework can be used for different representations and experimental results demonstrate that our approach significantly reduces the storage size with marginal distortion and achieves state-of-the-art rate-distortion performance for volumetric video on the HumanRF and ReRF datasets. Compared to the previous state-of-the-art method TeTriRF, we achieved an approximately -80% BD-rate on the HumanRF dataset and -60% BD-rate on the ReRF dataset.