Latent Fusion: Decoding Consolidated 3D Geometry from Feed-forward Geometry Transformer Latents

We propose a feed-forward method for dense Signed DistanceField (SDF) regression from unstructured image collections in less thanthree seconds, without camera calibration or post-hoc fusion. Our keyinsight is that the intermediate feature space of pretrained multi-viewfeed-forward geometry transformers (FFGT) already encodes a powerfuljoint world representation; yet, existing pipelines discard it, routing fea-tures through per-view prediction heads before assembling 3D geometrypost-hoc, which discards valuable completeness information and accu-mulates inaccuracies. We instead perform 3D extraction directly fromFFGT features via learned volumetric extraction: voxelized canonicalembeddings that progressively absorb multi-view geometry informationthrough interleaved cross- and self-attention into a structured volumet-ric latent grid. A simple convolutional decoder then maps this grid to adense SDF. We additionally propose a scalable, validity-aware supervi-sion scheme directly using SDFs derived from depth maps or 3D assets,tackling practical issues like non-watertight meshes. Our approach yieldscomplete and well-defined distance values across sparse- and dense-viewsettings and demonstrates geometrically plausible completions.