LoomMesh: Feed-forward Meshsplatting

3D Gaussian Splatting methods enable efficient optimisation and rendering, but are not natively supported by widely used, highly optimised mesh-based graph- ics engines (e.g., Unity, Blender). To exploit these engines, mesh-based neural rendering with triangle primitives has recently emerged, yet it typically relies on per-scene optimisation over many posed views, making it slow and hard to scale. We study feed-forward mesh splatting: creating a mesh directly from a single pair of posed images in one forward pass. This is challenging due to (1) sparse obser- vations and (2) the need to jointly infer vertex positions (i.e., geometry) and their connectivity (i.e., surface). To this end, we propose LoomMesh, which unpro- jects every pixel of each view to a 3D vertex carrying geometry and appearance features. We then connect the vertices in a candidate graph, linking each vertex to its same-view neighbours by pixel location and to its cross-view neighbours by geometric reprojection. To learn vertex geometry and connectivity jointly, we introduce a topology reasoning operator that, shared across all vertices, refines each vertex’s depth and decides which candidate faces to keep, passing evidence between neighbours along surfaces but not across their boundaries. The resulting mesh is rendered differentiably, so LoomMesh is trained end to end and needs no ground-truth meshes. Extensive experiments show that LoomMesh outperforms per-scene optimised methods in both visual fidelity and inference cost, as well as alternatives that combine feed-forward reconstruction with mesh conversion.