Revisiting Local Context for Long-Horizon Streaming 3D Reconstruction

Streaming 3D reconstruction from extremely long videos requires online estimation of camera motion and scene geometry with bounded model memory and per-frame computation. Early streaming models use finite context buffers or compact recurrent states, yet their estimates often deteriorate as sequences grow. Recent methods improve long-horizon stability through persistent or multi-level long-range memory. We pursue a different route: local geometric prediction with reliable sequential composition. We present LC3R, a simple streaming model that caches KV features from only the preceding 11 frames. It predicts a point map in the current camera coordinate system and an adjacent-frame relative pose, keeping prediction targets local as the stream grows. Global poses and geometry are recovered by composing these local measurements. To ensure reliable long-horizon composition, a lightweight temporal rotation refiner improves relative rotations using recent visual and motion context, while a composition-aware pose loss directly supervises multi-step pose composition. On Oxford Spires, LC3R achieves an ATE of 4.35 m and an RPE-R of $0.12^{\circ }$, reducing both errors by approximately 40% relative to the respective best prior streaming results. It runs at 24.45 FPS with 6.71 GB peak GPU memory, excluding input storage, on an NVIDIA H100 GPU.