  Approximating radiance fields with volumetric grids is one of promising
directions for improving NeRF, represented by methods like Plenoxels and DVGO,
which achieve super-fast training convergence and real-time rendering. However,
these methods typically require a tremendous storage overhead, costing up to
hundreds of megabytes of disk space and runtime memory for a single scene. We
address this issue in this paper by introducing a simple yet effective
framework, called vector quantized radiance fields (VQRF), for compressing
these volume-grid-based radiance fields. We first present a robust and adaptive
metric for estimating redundancy in grid models and performing voxel pruning by
better exploring intermediate outputs of volumetric rendering. A trainable
vector quantization is further proposed to improve the compactness of grid
models. In combination with an efficient joint tuning strategy and
post-processing, our method can achieve a compression ratio of 100$\times$ by
reducing the overall model size to 1 MB with negligible loss on visual quality.
Extensive experiments demonstrate that the proposed framework is capable of
achieving unrivaled performance and well generalization across multiple methods
with distinct volumetric structures, facilitating the wide use of volumetric
radiance fields methods in real-world applications. Code Available at
\url{https://github.com/AlgoHunt/VQRF}
