Solv3R: 3D Looped Transformer as a Multi-view Solver

Jeong-gi Kwak, Andrea Tagliasacchi, Sho Kagami, Yuki Ono, Kwang Moo Yi

Solv3R: 3D Looped Transformer as a Multi-view Solver: 17 upvotes on Hugging Face Daily Papers, #14 of 38 papers on 2026-11-05. Day-by-day upvote history.

Upvotes over time

It had 16 upvotes by its first day, 17 upvotes by the next day, 18 upvotes by day 3, 17 upvotes by day 7, and has 17 now. It ranked #79 of 207 papers in its week and #119 of 340 in its month. Its code repository has 285 GitHub stars.

Classical structure-from-motion reconstruction proceeds iteratively, refining camera poses and scene geometry from a current estimate. This allows maps to easily expand without recomputing already computed views, or geometry estimates to improve as more observations are provided. Most geometric foundation models, however, predict entire geometry in a single forward pass or update a recurrent state as frames arrive sequentially, making this expansion and refinement non-trivial. We introduce Solv3R, a looped transformer that learns to continue solving multi-view geometry from its current state. Importantly, its update operator is independent of the iteration count, allowing previously refined and newly introduced views to be jointly updated regardless of how ``solved'' they are. We train the operator to preserve accurate reconstructions under continued iteration and correct inaccurate states, enabling refinement of fixed image collections and integration of new observations. Representations of solved views can also be cached once and reused as geometric context across iterations, enabling compact and efficient key-value caching. We analyze the solver dynamics and compare it with the state-of-the-art, demonstrating that a single learned solver supports offline reconstruction, incremental reconstruction, relocalization, and streaming without task-specific training or modules. The code and models will be made publicly available at https://ubc-vision.github.io/solv3r/.

Paper page on Hugging Face · arXiv

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