CC-4DGS：用于存储高效的动态高斯溅射的计算变形与点云压缩
CC-4DGS: Computational Deformation and Point-Cloud Compression for Storage-Efficient Dynamic Gaussian Splatting
浏览论文内容
中文总结 AI 辅助
CC-4DGS是一种存储高效的动态高斯溅射框架，通过计算变形场和规范点云属性压缩，在保持实时渲染性能与相当重建精度的同时，大幅降低了场景存储需求。
中文摘要 AI 辅助
动态四维（4D）高斯溅射已成为高质量视图合成的强大显式表示，但现有方法因严重依赖大型多分辨率哈希表和高维高斯属性，每个场景仍需要数十到数百兆字节的存储空间。本文提出CC-4DGS，一种存储高效且可扩展的框架，重新思考变形建模和规范属性存储。首先，我们引入计算变形场（CDF），用确定性密集哈希编码和紧凑神经解码器取代大型多分辨率可学习哈希表，实现变形特征的实时合成，同时将变形存储减少到每个场景仅1-3 MB。其次，我们提出规范点云属性（CCA）压缩流水线，通过条件自动编码、选择性量化和残差码本压缩高维球谐外观项和辅助高斯属性，实现3-5倍的点云减少，且质量损失可忽略。这些组件共同构成统一表示，在保持实时渲染性能的同时将总存储减少到20-30 MB。在N3DV和Technicolor Light Field数据集上的大量实验表明，CC-4DGS实现了与Swift4D等最先进方法相当的重建精度，同时提供显著提升的存储效率和良好的运行时-内存权衡。
英文摘要
Dynamic four-dimensional (4D) Gaussian Splatting has emerged as a powerful explicit representation for high-quality view synthesis, yet existing methods still require tens to hundreds of megabytes per scene due to their heavy reliance on large multi-resolution hash tables and high-dimensional Gaussian attributes. This paper presents CC-4DGS, a storage-efficient and scalable framework that rethinks both deformation modeling and canonical attribute storage. First, we introduce a computational deformation field (CDF) that replaces large multi-resolution learnable hash tables with deterministic dense hash encoding and compact neural decoders, enabling on-the-fly synthesis of deformation features while reducing deformation storage to only 1--3 MB per scene. Second, we propose a compression of canonical point-cloud attributes (CCA) pipeline that compresses high-dimensional spherical harmonic appearance terms and auxiliary Gaussian attributes via conditional autoencoding, selective quantization, and residual codebooks, achieving 3--5$\times$ point-cloud reduction with negligible quality loss. Together, these components yield a unified representation that preserves real-time rendering performance while reducing total storage to 20--30 MB. Extensive experiments across the N3DV and Technicolor Light Field datasets demonstrate that CC-4DGS achieves reconstruction accuracy comparable to state-of-the-art methods such as Swift4D, while offering significantly improved storage efficiency and favorable runtime-memory trade-offs.
发表机构
- Hanyang University（汉阳大学）
机构由 AI 辅助整理，请以论文原文为准。
