AirSplat: Alignment and Rating for Robust Feed-Forward 3D Gaussian Splatting
arXiv cs.CV / 3/27/2026
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Key Points
- The paper introduces AirSplat, a training framework aimed at making 3D Vision Foundation Models more usable for pose-free, high-fidelity novel view synthesis (NVS) despite challenges in direct transfer.
- AirSplat’s Self-Consistent Pose Alignment (SCPA) adds a training-time feedback loop to align supervision at the pixel level and reduce pose–geometry mismatches.
- It also proposes Rating-based Opacity Matching (ROM), which uses a sparse-view NVS teacher’s local 3D consistency to filter out degraded 3D Gaussian primitives.
- Experiments on large-scale benchmarks report significantly improved reconstruction quality over existing state-of-the-art pose-free NVS methods.
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