|Given a set of 3D points that we know lie on the surface of an object, we can define many possible surfaces that pass through all of these points. Even when we consider only surface triangulations, there are still an exponential number of valid triangulations that all fit the data. Each triangulation will produce a different faceted surface connecting the points.
Our goal is to overcome this ambiguity and find the particular surface that is closest to the true object surface. We do not know the true surface but instead we assume that we have a set of images of the object. We propose selecting a triangulation based on its consistency with this set of images of the object. We present an algorithm that starts with an initial rough triangulation and refines the triangulation until it obtains a surface that best accounts for the images of the object. Our method is thus able to overcome the surface ambiguity problem and at the same time capture sharp corners and handle concave regions and occlusions. We show results for a few real objects.
Modeling by Videotape
Number of pages: 7
|Daniel D. Morris and Takeo Kanade, "Image-Consistent Surface Triangulation," Computer Vision and Pattern Recognition (CVPR 2000), July, 2000, pp. 332-338.|
author = "Daniel D. Morris and Takeo Kanade",
title = "Image-Consistent Surface Triangulation",
booktitle = "Computer Vision and Pattern Recognition (CVPR 2000)",
pages = "332-338",
publisher = "IEEE Computer Society",
month = "July",
year = "2000",
volume = "1",
|The Robotics Institute is part of the School of Computer Science, Carnegie Mellon University.|
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