Revisiting Local Context for Long-Horizon Streaming 3D Reconstruction

• URL: http://arxiv.org/abs/2608.27529v1
• Date: Thu, 27 Aug 2026 15:07:38 GMT
• Title: Revisiting Local Context for Long-Horizon Streaming 3D Reconstruction
• Abstract summary: ABot-Recon is a simple streaming model that caches KV features from only the preceding 11 frames.<n>It predicts a point map in the current camera coordinate system together with an adjacent-frame relative pose.<n>To reduce accumulated drift, a lightweight temporal refiner improves relative rotations using recent visual and motion context.
• Score: 10.383154504465232
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Streaming 3D reconstruction from extremely long videos requires estimating camera motion and scene geometry online under bounded memory and computation. Early streaming models achieve causal, bounded-cost inference using finite context buffers or compact recurrent states, yet their estimates often deteriorate as sequences grow. Recent methods improve long-horizon stability by coupling short-range context with persistent or multi-level long-range memory. We pursue a different route: we keep the learned temporal state strictly local and formulate predictions whose targets remain independent of sequence length. We present ABot-Recon, 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 together with an adjacent-frame relative pose. These predictions remain equivariant under changes of reference frame, and global poses and geometry are recovered through sequential composition. To reduce accumulated drift, a lightweight temporal refiner improves relative rotations using recent visual and motion context, while a composition-aware pose loss supervises multi-step pose composition. Extensive evaluations on challenging long-sequence benchmarks demonstrate the superior long-horizon performance of our local-context approach. On Oxford Spires, ABot-Recon achieves an ATE of 4.35 m and an RPE-R of $0.12^\circ$, reducing both errors by approximately 40\% relative to the best prior results.

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