A Flexible New Technique for Camera Calibration
IEEE Transactions on Pattern Analysis and Machine Intelligence
Convergence Properties of the Nelder--Mead Simplex Method in Low Dimensions
SIAM Journal on Optimization
Spatio-Temporal Stereo Using Multi-Resolution Subdivision Surfaces
International Journal of Computer Vision
International Journal of Computer Vision
Distinctive Image Features from Scale-Invariant Keypoints
International Journal of Computer Vision
IEEE Transactions on Pattern Analysis and Machine Intelligence
Multi-View Stereo Reconstruction and Scene Flow Estimation with a Global Image-Based Matching Score
International Journal of Computer Vision
Capturing and animating occluded cloth
ACM SIGGRAPH 2007 papers
Multi-scale 3D scene flow from binocular stereo sequences
Computer Vision and Image Understanding
Efficient Dense Scene Flow from Sparse or Dense Stereo Data
ECCV '08 Proceedings of the 10th European Conference on Computer Vision: Part I
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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.