FreeStreamGS: Online Feed-forward 3D Gaussian Splatting from Unposed Streaming Inputs
2026-06-02 • Computer Vision and Pattern Recognition
Computer Vision and Pattern Recognition
AI summaryⓘ
The authors address the challenge of creating high-quality 3D views from live, streaming images without knowing the camera poses in advance. They point out that previous methods struggle because small errors build up and ruin the image quality over time. Their solution, called FreeStreamGS, introduces techniques to keep the camera settings stable and fix errors in depth and pose estimation as new images come in. Experiments show their method produces results nearly as good as the best offline methods that have all images beforehand.
3D Gaussian SplattingNovel View SynthesisOnline RenderingCamera Pose EstimationMulti-view ConsistencyDepth EstimationPoint CloudIntrinsic Camera ParametersRendering Artifacts
Authors
Ruiyang Chen, Feiran Li, Chu Zhou, Zonglin Li, Zhanyu Ma, Heng Guo
Abstract
Feed-forward 3D Gaussian Splatting (3DGS) allows efficient and high-fidelity novel view synthesis (NVS) from an offline recorded image sequence. However, achieving online NVS from streaming and unposed image inputs remains challenging. Although online feed-forward geometric estimation methods have been proposed for streaming depth and point cloud recovery, they cannot be adapted to NVS due to severe rendering artifacts. This is because NVS demands stricter multi-view consistency in Gaussian scales and pose-geometry alignment; even minor deviations would accumulate over time and visibly degrade rendering quality. To this end, we propose FreeStreamGS, a robust online feed-forward framework for efficient and high-quality NVS. We introduce two key mechanisms: a Decoupled Intrinsic Recovery Head that removes cumulative camera intrinsic bias and prevents scene scale jitter during long-term streaming, and a Dynamic Point Refinement Offset strategy that relaxes rigid unprojection to correct coupled pose-depth drift. Extensive experiments show that FreeStreamGS achieves rendering quality competitive with state-of-the-art offline feed-forward 3DGS methods, despite operating without access to future frames.