GlobalSplat: Efficient Feed-Forward 3D Gaussian Splatting via Global Scene Tokens

摘要

The e_x001E_cient spatial allocation of primitives serves as thefoundation of 3D Gaussian Splatting, as it directly dictates the synergybetween representation compactness, reconstruction speed, and render-ing _x001C_delity. Previous solutions, whether based on iterative optimizationor feed-forward inference, su_x001B_er from signi_x001C_cant trade-o_x001B_s between thesegoals, mainly due to the reliance on local, heuristic-driven allocationstrategies that lack global scene awareness. Speci_x001C_cally, current feed-forward methods are largely pixel-aligned or primitive-aligned. By un-projecting pixels into dense, view-aligned primitives, they bake redun-dancy into the 3D asset. As more input views are added, the represen-tation size increases and global consistency becomes fragile. To this end,we introduce GlobalSplat, a framework built on the principle of align_x001C_rst, decode later. Our approach learns a compact, global, latent scenerepresentation that encodes multi-view input and resolves cross-view cor-respondences before decoding any explicit 3D geometry. Crucially, thisformulation enables compact, globally consistent reconstructions withoutrelying on pretrained pixel-prediction backbones or reusing latent fea-tures from dense baselines. Utilizing a coarse-to-_x001C_ne training curriculumthat gradually increases decoded capacity, GlobalSplat natively preventsrepresentation bloat. On RealEstate10K and ACID, our model achievescompetitive novel-view synthesis performance while utilizing as few as16K Gaussians, signi_x001C_cantly less than required by dense pipelines, ob-taining a light 4MB footprint. Further, GlobalSplat enables signi_x001C_cantlyfaster inference than the baselines, operating under 78 milliseconds in asingle forward pass.