Anzeige
Erschienen in:
2011 | OriginalPaper | Buchkapitel
Feature-Assisted Dense Spatio-temporal Reconstruction from Binocular Sequences
verfasst von : Yihao Zhou, Yan Qiu Chen
Erschienen in: Computer Vision – ACCV 2010
Verlag: Springer Berlin Heidelberg
Aktivieren Sie unsere intelligente Suche, um passende Fachinhalte oder Patente zu finden.
Wählen Sie Textabschnitte aus um mit Künstlicher Intelligenz passenden Patente zu finden. powered by
Markieren Sie Textabschnitte, um KI-gestützt weitere passende Inhalte zu finden. powered by
In this paper, a dynamic surface is represented by a triangle mesh with dense vertices whose 3D positions change over time. These time-varying positions are reconstructed by finding their corresponding projections in the images captured by two calibrated and synchronized video cameras. To achieve accurate dense correspondences across views and frames, we first match sparse feature points and rely on them to provide good initialization and strong constraints in optimizing dense correspondence. Spatio-temporal consistency is utilized in matching both features and image points. Three synergistic constraints, image similarity, epipolar geometry and motion clue, are jointly used to optimize stereo and temporal correspondences simultaneously. Tracking failure due to self-occlusion or large appearance change are automatically handled. Experimental results show that complex shape and motion of dynamic surfaces like fabrics and skin can be successfully reconstructed with the proposed method.