SOURCE-LINKED INTELLIGENCE
Revisiting Local Context for Long-Horizon Streaming 3D Reconstruction
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 s
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Evidence & attribution
- arXiv · AI, language, vision and robotics · 2026-08-27T15:07:38.000Z
First collected: 2026-09-21T08:32:02.028Z. This is not the publication date.