AVSplat:Dense-View Feed-Forward 3D Gaussian Splatting with Assist-View Preconditioning

Pose-free feed-forward 3D Gaussian Splatting enables novelview synthesis from uncalibrated multi-view images. Although more viewsshould improve performance, existing methods often degrade with dense-view inputs because global aggregation spreads attention over many to-kens, and naive voxel fusion averages many Gaussians into overly smoothrepresentations. We present AVSplat, a framework that turns additionalviews into reliable signals for both aggregation and representation. Be-fore global attention, each view performs a single lightweight interactionwith a small set of Assist Views chosen for relevance and diversity, andthe cached features provide a focused scene context that stabilizes corre-spondence. For representation, we use adaptive temperature-aware voxelfusion that sharpens attribution under high occupancy, guided by oc-cupancy and point confidence. Crucially, AVSplat restores positive viewscaling where performance remains stable or improves as more inputviews are added, instead of degrading in the dense-view regime. Abla-tions show that Assist View Preconditioning is primarily responsible forpreventing dense-view degradation, while Occupancy-guided Voxel Fu-sion contributes most of the single-point image-quality gains.