TED-4DGS：基于时序激活与嵌入变形的四维高斯点云压缩方法

TED-4DGS: Temporally Activated and Embedding-based Deformation for 4DGS Compression

December 5, 2025

作者: Cheng-Yuan Ho, He-Bi Yang, Jui-Chiu Chiang, Yu-Lun Liu, Wen-Hsiao Peng

cs.AI

摘要

基于3D高斯溅射（3DGS）在静态三维场景表示中的成功，其向动态场景的扩展（通常称为4DGS或动态3DGS）正受到日益广泛的关注。然而，如何为动态3DGS表示设计更紧凑高效的形变方案，并结合率失真优化的压缩策略，仍是研究尚不充分的领域。现有方法要么依赖时空4DGS中过度指定、存续时间短的高斯图元，要么采用缺乏显式时间控制的规范3DGS形变框架。为此，我们提出TED-4DGS——一种基于时序激活与嵌入的形变方案，通过融合两类方法的优势实现率失真优化的4DGS压缩。该方案建立在基于稀疏锚点的3DGS表示基础上：每个规范锚点被赋予可学习的时序激活参数以控制其在时间维度上的出现与消失过渡，同时通过轻量级锚点时序嵌入从共享形变库中查询生成锚点特定形变。在率失真压缩方面，我们引入基于隐式神经表示的超先验来建模锚点属性分布，并结合通道自回归模型捕捉锚点内部相关性。凭借这些创新设计，本方案在多个真实场景数据集上实现了最先进的率失真性能。据我们所知，这是首次针对动态3DGS表示构建率失真优化压缩框架的探索之一。

English

Building on the success of 3D Gaussian Splatting (3DGS) in static 3D scene representation, its extension to dynamic scenes, commonly referred to as 4DGS or dynamic 3DGS, has attracted increasing attention. However, designing more compact and efficient deformation schemes together with rate-distortion-optimized compression strategies for dynamic 3DGS representations remains an underexplored area. Prior methods either rely on space-time 4DGS with overspecified, short-lived Gaussian primitives or on canonical 3DGS with deformation that lacks explicit temporal control. To address this, we present TED-4DGS, a temporally activated and embedding-based deformation scheme for rate-distortion-optimized 4DGS compression that unifies the strengths of both families. TED-4DGS is built on a sparse anchor-based 3DGS representation. Each canonical anchor is assigned learnable temporal-activation parameters to specify its appearance and disappearance transitions over time, while a lightweight per-anchor temporal embedding queries a shared deformation bank to produce anchor-specific deformation. For rate-distortion compression, we incorporate an implicit neural representation (INR)-based hyperprior to model anchor attribute distributions, along with a channel-wise autoregressive model to capture intra-anchor correlations. With these novel elements, our scheme achieves state-of-the-art rate-distortion performance on several real-world datasets. To the best of our knowledge, this work represents one of the first attempts to pursue a rate-distortion-optimized compression framework for dynamic 3DGS representations.