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}

翻译：使用体积网格的光亮场是改善NERF的有希望的方向之一,其表现形式为Plenoxels和DVGO等方法,这些方法实现了超快培训趋同和实时转换,然而,这些方法通常需要巨大的存储间接费用,耗资多达数百兆字节的磁盘空间和单个场景的运行时间记忆。我们在本文件中处理这一问题时,采用了一个简单而有效的框架,称为矢量量化的光亮场(VQRF),以压缩这些以体积网格为基础的发光场。我们首先为估计电网模型冗余量和通过更好地探索体积成品的中间输出来进行 voxel 运行,我们提出了一个强有力和适应性强的衡量标准。还进一步提议了一种可训练的矢量量量量量化,以提高电网模模型的紧凑性。结合有效的联合调控战略和后处理,我们的方法可以实现100美元的时间压缩率,将总模型尺寸降至1MB,在视觉质量上损失微不足道。广泛的实验表明,拟议的框架能够实现无价值的性性性性性性性工作,并且在现实/RFRFRDRAreareareal应用中,便利广泛的广泛应用方法。