CLoSeR: Closing the Loop for Long-Context Streaming Reconstruction

CLoSeR detects loop candidates via global descriptors to close loops in streaming reconstruction, reducing drift and producing consistent kilometer-scale geometry with SE(3) pose optimization.

Moyang Li, Zihan Zhu, Wei Zhang, Marc Pollefeys, Daniel Barath

Published 2026Paris Poster Session 1 · Wed, Dec 9, 12:30 PM–2:30 PM local time · Paris Poster HallarXiv ↗OpenReview ↗

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Abstract

Feedforward foundation models have recently shown remarkable 3D reconstruction capabilities. However, existing models exhibit large tracking drift in long-context streaming reconstruction due to error accumulation. In this paper, we revisit loop closure with streaming reconstruction foundation models to enable accurate, drift-free, kilometer-scale reconstruction. Specifically, our method detects loop candidates through global descriptor retrieval, and constructs loop-conditioned windows to estimate the relative poses between looped frames. Given the observation that our adopted streaming reconstruction backbone produces a globally consistent scale, we optimize all frame poses on the SE(3) manifold with sequential and loop closure constraints, avoiding the pose graph optimization on the Sim(3) or higher-dimensional SL(4) manifolds employed in prior works. Extensive experiments show that our method reduces drift and produces consistent geometry on kilometer-scale sequences, significantly outperforming the state of the art. Code is available at https://github.com/MoyangLi00/CLoSeR.git.