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arXiv Computer Vision By Chin-Yang Lin, Yang-Che Sun, Cheng Sun, Fu-En Yang, Min-Hung Chen, Yen-Yu Lin, Wei-Chen Chiu, Yu-Lun Liu

Scal3R: Learning Efficient Multi-Relative Pose Query for Scalable Online 3D Reconstruction

Read the original on arXiv Computer Vision →

Scal3R is a new method for online 3D reconstruction that addresses the failure of traditional models on long videos by decoupling per‑frame depth from global pose estimation. It reformulates reconstruction as a multi‑reference relative pose query, using lightweight learnable tokens (~1% of parameters) injected into a frozen backbone via asymmetric attention to query poses relative to multiple past keyframes. An online pose‑graph optimization with loop closure further suppresses drift, achieving convergence in 8 hours on a single GPU and reducing average absolute trajectory error by over 60% on KITTI while setting state‑of‑the‑art results on several benchmark datasets.

Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at arXiv Computer Vision.

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