Surflo: Consistent 3D Surface Flow Model with Global State
Summary
Surflo is a feed-forward 3D reconstruction model that compresses unposed RGB views into latent tokens and decodes consistent 3D surface points via flow matching, enabling variable-resolution output and outperforming existing methods in speed.
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Paper page - Surflo: Consistent 3D Surface Flow Model with Global State
Source: https://huggingface.co/papers/2606.13644
Abstract
Surflo compresses unposed RGB views into latent tokens and decodes 3D surface points through flow matching, enabling flexible resolution output and efficient processing compared to existing methods.
Geometry is invariant to viewpoint, which makes any collection of images a redundant encoding of a single 3D state. Existingfeed-forward reconstruction modelsfail to exploit this: per-view methods emit overlapping, unalignedpointmapsthat grow linearly with input count, whileglobal-latent methodscommit to a fixed, low-resolution output. We introduce Surflo, which compresses a variable number of unposed RGB views into Klatent tokens-one global state-and decodes oriented 3D surface points by independently transporting them from noise onto the surface viaflow matching. This frees the output from any fixed grid or token budget: the same latent yields from a few thousand to a million points in a single forward pass. To suppress the local inconsistencies inherent to independent per-point decoding, an inference-time guidance term correlates nearby points by injecting aphotometric gradientduringODE integration. Surflo matches or surpasses feed-forward baselines onsurface metrics, runs an order of magnitude faster than optimization-based methods that require hundreds of views, and is the only feed-forward approach to combine a global latent with arbitrary-resolution decoding.
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