Computing Differential Properties of 3-D Shapes from Stereoscopic Images without 3-D Models

Frédéric Devernay 1 Olivier Faugeras 1
1 ROBOTVIS - Computer Vision and Robotics
CRISAM - Inria Sophia Antipolis - Méditerranée
Abstract : We are considering the problem of recovering the three-dimensional geometry of a scene from binocular stereo disparity. Once a dense disparity map has been computed from a stereo pair of images, one often needs to calculate some local differential properties of the corresponding 3-D surface such as orientation or curvatures. The usual approach is to build a 3-D reconstruction of the surface(s) from which all shape properties will then be derived without ever going back to the original images. In this paper, we depart from this paradigm and propose to use the images directly to compute the shape properties. We thus propose a new method extending the classical correlation method to estimate accurately both the disparity and its derivatives directly from the image data. We then relate those derivatives to differential properties of the surface such as orientation and curvatures.
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Communication dans un congrès
CVPR - International Conference on Computer Vision and Pattern Recognition - 1994, 1994, Seattle, United States. IEEE, pp.208 - 213, 1994, Computer Vision and Pattern Recognition, 1994. Proceedings. <10.1109/CVPR.1994.323831>
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Frédéric Devernay, Olivier Faugeras. Computing Differential Properties of 3-D Shapes from Stereoscopic Images without 3-D Models. CVPR - International Conference on Computer Vision and Pattern Recognition - 1994, 1994, Seattle, United States. IEEE, pp.208 - 213, 1994, Computer Vision and Pattern Recognition, 1994. Proceedings. <10.1109/CVPR.1994.323831>. <hal-00816702>

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