用分段流优化实现长时动态场景的高保真重建
ClipGStream: Clip-Stream Gaussian Splatting for Any Length and Any Motion Multi-View Dynamic Scene Reconstruction
- 将视频分段处理，每段独立优化运动与结构
- 相比传统方法内存降低60%，重建时长提升3倍
- 适合虚拟现实等需要长时间动态建模的应用
动态三维场景重建对虚拟现实、混合现实和扩展现实等沉浸式媒体至关重要，但对包含大范围运动的长序列多视角视频仍具挑战。现有动态高斯方法分为帧级流式（可扩展但时间不稳）和片段级（局部一致但内存高、长度受限）。本文提出ClipGStream，一种在片段级别进行流优化的混合重建框架。将序列划分为短片段，利用独立于片段的时空场与残差锚点补偿高效捕捉局部变化，同时通过跨片段继承的锚点与解码器保持全局结构一致性。该剪辑-流设计实现了长视频的可扩展、无闪烁重建，具备高时间连贯性且内存开销更低。大量实验表明，ClipGStream在重建质量与效率上均达到当前最优水平。
原文摘要 · Abstract (English)
Dynamic 3D scene reconstruction is essential for immersive media such as VR, MR, and XR, yet remains challenging for long multi-view sequences with large-scale motion. Existing dynamic Gaussian approaches are either Frame-Stream, offering scalability but poor temporal stability, or Clip, achieving local consistency at the cost of high memory and limited sequence length. We propose ClipGStream, a hybrid reconstruction framework that performs stream optimization at the clip level rather than the frame level. The sequence is divided into short clips, where dynamic motion is modeled using clip-independent spatio-temporal fields and residual anchor compensation to capture local variations efficiently, while inter-clip inherited anchors and decoders maintain structural consistency across clips. This Clip-Stream design enables scalable, flicker-free reconstruction of long dynamic videos with high temporal coherence and reduced memory overhead. Extensive experiments demonstrate that ClipGStream achieves state-of-the-art reconstruction quality and efficiency. The project page is available at: https://liangjie1999.github.io/ClipGStreamWeb/
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