A Method for Registration of 3-D Shapes
IEEE Transactions on Pattern Analysis and Machine Intelligence - Special issue on interpretation of 3-D scenes—part II
Mean Shift: A Robust Approach Toward Feature Space Analysis
IEEE Transactions on Pattern Analysis and Machine Intelligence
A Taxonomy and Evaluation of Dense Two-Frame Stereo Correspondence Algorithms
International Journal of Computer Vision
Match Propogation for Image-Based Modeling and Rendering
IEEE Transactions on Pattern Analysis and Machine Intelligence
Mean Shift, Mode Seeking, and Clustering
IEEE Transactions on Pattern Analysis and Machine Intelligence
Epiflow Quadruplet Matching: Enforcing Epipolar Geometry for Spatio-Temporal Stereo Correspondences
WACV-MOTION '05 Proceedings of the Seventh IEEE Workshops on Application of Computer Vision (WACV/MOTION'05) - Volume 1 - Volume 01
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3-D reconstruction from images sequences has been the center topic of computer vision. Real-time applications call for causal processing of stereo sequences, as they are acquired, covering different regions of the scene. The first step is to compute the current stereo disparity, and recursive map building often requires fusing with the previous estimate. In this paper, the epiflow framework [1], originally proposed for establishing matches among stereo feature pairs is generalized to devise an iterative causal algorithm for stereo disparity map fusion. In the context of disparity fusion, quadruplet correspondence of the epiflow tracking algorithm becomes reminiscent of the "closest point" of the 3-D ICP algorithm. Unlike ICP, the 2-D epiflow framework permits incorporating both photometric and geometrical constraints, estimation of the stereo rig motion as supplementary information, as well as identifying local inconsistencies between the two disparity maps. Experiments with real data validate the proposed approach, and improved converge compared to the ICP algorithm.