Abstract
3D Gaussian Splatting (3DGS) has demonstrated remarkable effectiveness in 3D reconstruction, achieving high-quality results with real-time radiance field rendering. However, a key challenge is the substantial storage cost: reconstructing a single scene typically requires millions of Gaussian splats, each represented by 59 floating-point parameters, resulting in approximately 1~GB of memory. To address this challenge, we propose a compression method by building separate attribute codebooks and storing only discrete code indices. Specifically, we employ noise-substituted vector quantization technique to jointly train the codebooks and model features, ensuring consistency between gradient descent optimization and parameter discretization. Our method reduces the memory consumption efficiently (around $45\times$) while maintaining competitive reconstruction quality on standard 3D benchmark scenes. Experiments on different codebook sizes show the trade-off between compression ratio and image quality. Furthermore, the trained compressed model remains fully compatible with popular 3DGS viewers and enables faster rendering speed, making it well-suited for practical applications.
Abstract (translated)
3D高斯点阵（3DGS）在三维重建中表现出显著的有效性，能够实现高质量的结果，并且支持实时辐射场渲染。然而，一个关键的挑战在于其巨大的存储成本：重构单个场景通常需要数百万个高斯点阵，每个点阵由59个浮点参数表示，总计大约需要1GB的内存空间。为了解决这一问题，我们提出了一种压缩方法，即构建独立的属性代码本并仅存储离散化的代码索引。具体来说，我们采用噪声替代向量量化技术来联合训练代码本和模型特征，确保梯度下降优化与参数离散化之间的一致性。我们的方法能够有效地减少内存消耗（约45倍），同时在标准的3D基准场景上保持竞争性的重建质量。不同大小代码本上的实验表明了压缩比率与图像质量之间的权衡关系。此外，训练好的压缩模型完全兼容流行的3DGS查看器，并且支持更快的渲染速度，使其非常适合实际应用。
URL
https://arxiv.org/abs/2504.03059
