A maximum likelihood stereo algorithm
Computer Vision and Image Understanding
Visual motion of curves and surfaces
Visual motion of curves and surfaces
Finding the collineation between two projective reconstructions
Computer Vision and Image Understanding
Multiple view geometry in computer vision
Multiple view geometry in computer vision
A Hierarchical Symmetric Stereo Algorithm Using Dynamic Programming
International Journal of Computer Vision
IEEE Transactions on Pattern Analysis and Machine Intelligence
The Visual Hull Concept for Silhouette-Based Image Understanding
IEEE Transactions on Pattern Analysis and Machine Intelligence
Multi Viewpoint Stereo from Uncalibrated Video Sequences
ECCV '98 Proceedings of the 5th European Conference on Computer Vision-Volume I - Volume I
Stereo Matching Using Belief Propagation
IEEE Transactions on Pattern Analysis and Machine Intelligence
Geometric modeling for computer vision.
Geometric modeling for computer vision.
ICCV '03 Proceedings of the Ninth IEEE International Conference on Computer Vision - Volume 2
Silhouette and stereo fusion for 3D object modeling
Computer Vision and Image Understanding - Model-based and image-based 3D scene representation for interactive visalization
A Comparison and Evaluation of Multi-View Stereo Reconstruction Algorithms
CVPR '06 Proceedings of the 2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition - Volume 1
Shape recovery from turntable sequence using rim reconstruction
Pattern Recognition
A hybrid approach for computing visual hulls of complex objects
CVPR'03 Proceedings of the 2003 IEEE computer society conference on Computer vision and pattern recognition
Carved visual hulls for image-based modeling
ECCV'06 Proceedings of the 9th European conference on Computer Vision - Volume Part I
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This paper presents a new algorithm for 3D shape recovery from an image sequence captured under circular motion. The algorithm recovers the 3D shape by reconstructing a set of 3D rim curves, where a 3D rim curve is defined by the two frontier points arising from two views. The idea consists of estimating the position of each point of the 3D rim curve by using three views. Specifically, two of these views are chosen close to each other in order to guarantee a good image point matching, while the third view is chosen far from these two views in order to compensate for the error introduced in the triangulation scheme by the short baseline of the two close views. Image point matching among all views is performed by a new method which suitably combines epipolar geometry and cross-correlation. The algorithm is illustrated through experiments with synthetic and real data, which show satisfactory and promising results.