LifeStreamGS: Lifecycle-Aware Streaming 3D Gaussian Splatting for Unposed Videos

Streaming feed-forward 3D Gaussian Splatting (3DGS) reconstructs renderable scenes from unposed video streams. In this setup, each frame's Gaussians are predicted without future observations and remain fixed as new frames arrive, causing prediction errors to accumulate. Dense per-pixel predictions further amplify this problem by adding redundant Gaussians, which increasingly degrade rendering quality over long sequences. To this end, we propose LifeStreamGS, a lifecycle-aware framework that treats dense Gaussian predictions as candidates and controls their placement, retention, and contribution to rendering. These decisions are implemented through merge-guided Gaussian proposal, existence-aware candidate validation, and view- and time-aware opacity aging, reducing redundancy and the impact of accumulated errors. Furthermore, for long videos, we extend this framework through chunk-wise streaming, reconstructing overlapping windows and aligning their reconstructions into a shared coordinate system. Experiments show competitive novel-view synthesis quality with lower runtime and GPU memory usage than the evaluated optimization-based baselines in most tested settings, while supporting 500-view reconstruction and online video stabilization.