# Streaming 3D Reconstruction

Streaming 3D foundation models estimate camera poses and dense geometry online, but each prediction is fixed once emitted, so drift over long sequences cannot be corrected by loop closure. Map2SLAM keeps LingBot-Map frozen, trains only a lightweight retrieval head, and uses the model's causal streaming inference for front-end tracking and its bidirectional prefix inference for back-end re-observation, turning predictions into an external, revisable SLAM state. On KITTI, VBR and Oxford Spires it reaches a mean ATE 42.7%–48.3% below the best uncalibrated baselines while tracking in real time above 28 FPS on an NVIDIA RTX 5090D.

Streaming 3D foundation models estimate camera poses and dense geometry online, but each prediction is fixed once emitted, so drift over long sequences cannot be corrected by loop closure. Map2SLAM keeps LingBot-Map frozen, trains only a lightweight retrieval head, and uses the model's causal streaming inference for front-end tracking and its bidirectional prefix inference for back-end re-observation, turning predictions into an external, revisable SLAM state. On KITTI, VBR and Oxford Spires it reaches a mean ATE 42.7%–48.3% below the best uncalibrated baselines while tracking in real time above 28 FPS on an NVIDIA RTX 5090D.