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arXiv · 2606.03254

OF$^3$GS: On-the-Fly Feed-Forward 3D Gaussian Splatting from Unposed Images

Ruiyang Chen·Feiran Li·Chu Zhou·Zonglin Li·Zhanyu Ma·Heng Guo
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Abstract

Feed-forward 3D Gaussian Splatting (3DGS) enables efficient and high-fidelity novel view synthesis (NVS) from offline image sequences. However, achieving on-the-fly NVS from unposed images remains challenging: the system must reconstruct renderable 3D Gaussians as images arrive, without access to future observations. Although online feed-forward geometry methods have been developed for causal depth and point-cloud recovery, directly adapting them to NVS often leads to severe rendering artifacts because Gaussian-based rendering demands stricter multi-view consistency in primitive scale and pose-geometry alignment. Even minor deviations can accumulate under causal inference and visibly degrade rendering quality. To this end, we propose OF$^3$GS, a feed-forward framework for efficient and high-quality on-the-fly NVS from sparse-view unposed images under causal constraints. We introduce two mechanisms for causal geometric stability: a Decoupled Intrinsic Recovery Head that mitigates cumulative camera-intrinsic bias and scene-scale jitter, and Dynamic Point Refinement Offsets that relax rigid unprojection to compensate for coupled pose-depth drift. Extensive experiments show that OF$^3$GS outperforms online baselines and approaches offline feed-forward 3DGS methods under comparable sparse-input settings. It also remains memory-feasible with denser inputs. Homepage: https://richardchen225.github.io/of3gs/

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Ruiyang Chen, Feiran Li, Chu Zhou, Zonglin Li, Zhanyu Ma, Heng Guo. 2026-06-02. OF$^3$GS: On-the-Fly Feed-Forward 3D Gaussian Splatting from Unposed Images. https://arxiv.org/abs/2606.03254

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