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arXiv · 2603.25129

AirSplat: Alignment and Rating for Robust Feed-Forward 3D Gaussian Splatting

Minh-Quan Viet Bui·Jaeho Moon·Munchurl Kim
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Abstract

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 sparse-view NVS teacher model to filter out degraded primitives. Experimental results on large-scale benchmarks demonstrate that our method significantly outperforms state-of-the-art pose-free NVS approaches in reconstruction quality. Our AirSplat highlights the potential of adapting 3DVFMs to enable simultaneous visual geometry estimation and high-quality view synthesis.

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Minh-Quan Viet Bui, Jaeho Moon, Munchurl Kim. 2026-03-26. AirSplat: Alignment and Rating for Robust Feed-Forward 3D Gaussian Splatting. https://arxiv.org/abs/2603.25129

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