RoEL: イベントカメラによる堅牢な3Dライン再構成
RoEL: Robust Event-based 3D Line Reconstruction
イベントカメラのデータからライン構造を安定的に抽出し、幾何学的コスト関数で3Dラインマップとカメラ姿勢を精緻化する手法を提案。多様なデータセットでマッピングと姿勢推定の性能を大幅に向上させ、マルチモーダルにも適用可能。
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著者: Gwangtak Bae, Jaeho Shin, Seunggu Kang, Junho Kim, Ayoung Kim, Young Min Kim
分類: cs.RO, cs.CV
原文アブストラクト
Event cameras in motion tend to detect object boundaries or texture edges, which produce lines of brightness changes, especially in man-made environments. While lines can constitute a robust intermediate representation that is consistently observed, the sparse nature of lines may lead to drastic deterioration with minor estimation errors. Only a few previous works, often accompanied by additional sensors, utilize lines to compensate for the severe domain discrepancies of event sensors along with unpredictable noise characteristics. We propose a method that can stably extract tracks of varying appearances of lines using a clever algorithmic process that observes multiple representations from various time slices of events, compensating for potential adversaries within the event data. We then propose geometric cost functions that can refine the 3D line maps and camera poses, eliminating projective distortions and depth ambiguities. The 3D line maps are highly compact and can be equipped with our proposed cost function, which can be adapted for any observations that can detect and extract line structures or projections of them, including 3D point cloud maps or image observations. We demonstrate that our formulation is powerful enough to exhibit a significant performance boost in event-based mapping and pose refinement across diverse datasets, and can be flexibly applied to multimodal scenarios. Our results confirm that the proposed line-based formulation is a robust and effective approach for the practical deployment of event-based perceptual modules. Project page: https://gwangtak.github.io/roel/
関連論文
- リフォーカスイベント融合によるマルチイベントカメラ深度推定と外れ値除去イベントカメラ/3D再構成
