论文标题
压缩体积辐射场至1 MB
Compressing Volumetric Radiance Fields to 1 MB
论文作者
论文摘要
具有体积网格的近似辐射场是改善NERF的有前途的方向之一，该方法由全体氧和DVGO等方法表示，这些方法可实现超快速的训练收敛和实时渲染。但是，这些方法通常需要巨大的存储开销，用于一个场景的数百兆字节的磁盘空间和运行时内存。我们在本文中通过引入一个简单但有效的框架（称为向量量化的辐射场（VQRF））来解决此问题，以压缩这些基于体积网格的辐射率字段。我们首先提出了一个可靠和适应性的度量标准，用于通过更好地探索体积渲染的中间输出来估算网格模型中的冗余和进行体素修剪。进一步提出了可训练的矢量量化，以改善网格模型的紧凑性。结合有效的联合调整策略和后处理，我们的方法可以通过将整体型号的大小减少到1 MB，而视觉质量可忽略不计，可以实现100 $ \ times $的压缩比。广泛的实验表明，所提出的框架能够在具有独特的体积结构的多种方法上实现无与伦比的性能和良好的概括，从而促进了在现实世界应用中广泛使用体积辐射场方法。代码可在\ url {https://github.com/algohunt/vqrf}中获得代码
Approximating radiance fields with volumetric grids is one of promising directions for improving NeRF, represented by methods like Plenoxels and DVGO, which achieve super-fast training convergence and real-time rendering. However, these methods typically require a tremendous storage overhead, costing up to hundreds of megabytes of disk space and runtime memory for a single scene. We address this issue in this paper by introducing a simple yet effective framework, called vector quantized radiance fields (VQRF), for compressing these volume-grid-based radiance fields. We first present a robust and adaptive metric for estimating redundancy in grid models and performing voxel pruning by better exploring intermediate outputs of volumetric rendering. A trainable vector quantization is further proposed to improve the compactness of grid models. In combination with an efficient joint tuning strategy and post-processing, our method can achieve a compression ratio of 100$\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}
