ATSplat: Compact Feed-forward 3D Gaussian Splatting with Adaptive Token Expansion

3D Gaussian Splatting (3DGS) achieves high-quality novel-view synthesis by optimizing freely placed primitives in 3D and adaptively densifying them in under-reconstructed regions. However, this scene-adaptive capacity allocation is largely lost in existing feed-forward 3DGS methods, where primitive counts and placement depend on input image grids rather than scene complexity. We present ATSplat, a feed-forward 3DGS framework that restores scene-adaptive capacity allocation through Adaptive 3D Tokens. ATSplat first lifts coarse patch-level depth and camera cues into sparse 3D anchor tokens, forming a compact scene scaffold. The tokens are progressively and selectively expanded and refined during decoding, guided by an Adaptive Token Expansion module that predicts per-token expansion scores at each stage. The expansion scores are supervised by the rendering error reduction associated with token expansion. Each refined token is then decoded into local Gaussians with learnable 3D offsets, decoupling primitive placement from input image grids. Experiments on the RealEstate10K and DL3DV datasets highlight that ATSplat achieves competitive rendering quality while using up to $61\times$ fewer Gaussians than dense pixel-aligned 3DGS methods.