AirSplat: Alignment and Rating for Robust Feed-Forward 3D Gaussian Splatting
While 3D Vision Foundation Models (3DVFMs) have demonstrated remarkable zero-shot capabilities in visual geometry estimation, their direct application to generalizable novel view synthesis (NVS) remains challenging. In this paper, we propose AirSplat, a novel training framework that effectively adapts the robust geometric priors of 3DVFMs into high-fidelity, pose-free NVS. Our approach introduces two key technical contributions: (1) Self-Consistent Pose Alignment (SCPA), a training-time feedback loop that ensures pixel-aligned supervision to resolve pose-geometry discrepancy; and (2) Rating-based Opacity Matching (ROM), which leverages the local 3D geometry consistency knowledge from a sp...
| Authors | Minh-Quan Viet Bui, Jaeho Moon, Munchurl Kim |
|---|---|
| Primary category | cs.CV |
| Published | 26 March 2026 |
| Last updated at source | 2 August 2026 |
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