ZipSplat: Fewer Gaussians, Better Splats
Feed-forward 3D Gaussian Splatting methods reconstruct a scene from posed or pose-free images in a single forward pass, yet current approaches predict one Gaussian per input pixel, tying the representation budget to camera resolution rather than scene complexity. A flat wall and a richly textured object thus produce equally many Gaussians despite very different geometric needs. We propose ZipSplat, a token-based feed-forward model that decouples Gaussian placement from the pixel grid. A multi-view backbone extracts dense visual tokens, and k-means clustering compresses them into a compact set of scene tokens. Cross- and self-attention refine these tokens, and a lightweight MLP decodes eac...
| Authors | Alexander Veicht, Sunghwan Hong, Dániel Baráth et al. |
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| Primary category | cs.CV |
| Published | 3 June 2026 |
| Last updated at source | 12 June 2026 |
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