CLoSeR: Closing the Loop for Long-Context Streaming Reconstruction

Summary

Abstract Summary

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.

Key Points

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.

Method

Our proposed method, CLoSeR , enables accurate kilometer-scale streaming reconstruction from monocular sequences by augmenting a feedforward backbone ( Zhang et al., 2026 ) with a loop closure module. In Sec. 3.1 , we introduce the preliminaries of the streaming reconstruction backbone that our framework builds upon. In Sec. 3.2 , we present loop detection and relative pose estimation between looped frames.

Finally, in Sec. 3.3 , we describe the global pose graph optimization over all frames with sequential and loop closure constraints.

Method — CLoSeR: Closing the Loop for Long-Context Streaming Reconstruction — Figure 2 : System overview. Given a streaming monocular sequence, our method processes it in a sliding-window manner. For each window, we first tokenize the frames with a DINO-based encoder and then apply a stack of attention layers, comprising frame attention, sliding-window attention, and TTT layers, to maintain global consistency across the sequence. Upon the arrival of each new window, we additionally extract SALAD descriptors to identify potential loop closures against prior windows. When such candidates are detected, we construct a loop-conditioned window by pairing $N_{\text{win}}/2$ frames from the current window with $N_{\text{win}}/2$ frames from the corresponding prior window, predict the relative poses between loop edges from a single forward pass, and perform pose graph optimization on the $\mathrm{SE}(3)$ manifold to correct tracking drift.

Experiments

We evaluate our method on (a) three representative long-context reconstruction benchmarks: the VBR dataset ( Brizi et al., 2024 ) , the autonomous driving benchmark KITTI Odometry ( Geiger et al., 2013 ) , and the challenging Oxford Spires dataset ( Tao et al., 2024 ) ; and (b) one outdoor dynamic benchmark, DROID-W ( Li et al., 2026 ) .

VBR is captured in Rome and provides RGB sequences, 3D point clouds, IMU measurements, and ground-truth trajectories obtained via bundle adjustment.

Experiments — CLoSeR: Closing the Loop for Long-Context Streaming Reconstruction — Figure 3 : Reconstruction comparison. LoGeR ( Zhang et al., 2026 ) fails to close loops and produces misaligned duplicate geometry, whereas our method recovers globally consistent reconstructions. Key regions are highlighted with red dashed boxes.

Sources and Demonstrations

Figures are reproduced from Li et al. See the full paper for experimental details and the project page, when the paper links one, for demonstrations and videos.