VidMap: 時間構造を活用したビデオベースのStructure-from-Motion
VidMap: Exploiting Temporal Structure for Video-Based Structure-from-Motion
SLAMの逐次制約とSfMの大域最適化を組み合わせ、任意の長い非校正ビデオからメートルスケールのカメラ軌道を頑健に復元するシステムを提案した。
詳しい要約
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2. 先行研究と比べてどこがすごい？
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著者: Zador Pataki, Paul-Edouard Sarlin, Marc Pollefeys
分類: cs.CV, cs.RO
原文アブストラクト
Accurately recovering the camera's calibration and metric poses for any unconstrained video would unlock large-scale training data for navigation and scene understanding. The dominant approaches to this problem are severely limited: Simultaneous Localization and Mapping (SLAM) is sensitive to initialization and transient failures due to its causal, incremental nature; it is often over-optimized for real-time operation and generally requires known camera calibration; while Structure-from-Motion (SfM) typically forgoes any image ordering, enabling optimal initialization and global optimization, but lacks robustness to visual symmetries and extreme motions. To bridge this gap, we introduce a system that combines the strong sequential constraints of SLAM with the flexibility and global optimization of offline SfM, enabling the metric reconstruction of arbitrary, long, uncalibrated videos. This system leverages recent advances in wide-baseline dense image matching, treats temporal ordering as a first-class citizen for reliable loop closure, and augments global optimization with metric monocular depth priors. As a result, thorough evaluations on diverse, challenging datasets that exhibit extreme motion and visual symmetries reveal that our approach is significantly more robust and accurate than both state-of-the-art SLAM and SfM, classical or learned, with given or unknown camera calibration. The code is publicly available at https://github.com/cvg/vidmap.
