通过场景自适应晶格向量量化改进3D高斯散射压缩
Improving 3D Gaussian Splatting Compression by Scene-Adaptive Lattice Vector Quantization
AI总结：
本文提出了一种场景自适应晶格向量量化（SALVQ）方法，用于改进3D高斯散射（3DGS）的压缩性能，通过优化晶格基矢来提高适应性和R-D效率，同时减少计算开销和训练时间。
摘要：
3D高斯散射（3DGS）因其逼真渲染质量和实时性能而迅速流行，但会产生大量数据。因此，压缩3DGS数据对于其模型的成本效益至关重要。最近，一些基于锚点的神经压缩方法已被提出，实现了良好的3DGS压缩性能。然而，它们都依赖于统一标量量化（USQ）因其简单性。一个引人注目的问题是，更复杂的量化器是否能在极小的额外开销和系统最小变化的情况下改进当前的3DGS压缩方法。答案是肯定的，通过将USQ替换为晶格向量量化（LVQ）。为了更好地捕捉场景特定特性，我们为每个场景优化晶格基矢，提高LVQ的适应性和R-D效率。这种场景自适应LVQ（SALVQ）在向量量化和USQ的低复杂性之间取得了平衡。SALVQ可以无缝集成到现有的3DGS压缩架构中，通过最小的修改和计算开销提高其R-D性能。此外，通过缩放晶格基矢量，SALVQ可以动态调整晶格密度，使单个模型能够适应多种比特率目标。这种灵活性消除了为不同压缩级别训练单独模型的需要，显著减少了训练时间和内存消耗。
英文摘要：
3D Gaussian Splatting (3DGS) is rapidly gaining popularity for its photorealistic rendering quality and real-time performance, but it generates massive amounts of data. Hence compressing 3DGS data is necessary for the cost effectiveness of 3DGS models. Recently, several anchor-based neural compression methods have been proposed, achieving good 3DGS compression performance. However, they all rely on uniform scalar quantization (USQ) due to its simplicity. A tantalizing question is whether more sophisticated quantizers can improve the current 3DGS compression methods with very little extra overhead and minimal change to the system. The answer is yes by replacing USQ with lattice vector quantization (LVQ). To better capture scene-specific characteristics, we optimize the lattice basis for each scene, improving LVQ's adaptability and R-D efficiency. This scene-adaptive LVQ (SALVQ) strikes a balance between the R-D efficiency of vector quantization and the low complexity of USQ. SALVQ can be seamlessly integrated into existing 3DGS compression architectures, enhancing their R-D performance with minimal modifications and computational overhead. Moreover, by scaling the lattice basis vectors, SALVQ can dynamically adjust lattice density, enabling a single model to accommodate multiple bit rate targets. This flexibility eliminates the need to train separate models for different compression levels, significantly reducing training time and memory consumption.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
