Three-dimensional object recognition from single two-dimensional images (1987)
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| Venue: | Artificial Intelligence |
| Citations: | 303 - 6 self |
BibTeX
@ARTICLE{Lowe87three-dimensionalobject,
author = {David G. Lowe},
title = {Three-dimensional object recognition from single two-dimensional images},
journal = {Artificial Intelligence},
year = {1987},
pages = {355--395}
}
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Abstract
A computer vision system has been implemented that can recognize threedimensional objects from unknown viewpoints in single gray-scale images. Unlike most other approaches, the recognition is accomplished without any attempt to reconstruct depth information bottom-up from the visual input. Instead, three other mechanisms are used that can bridge the gap between the two-dimensional image and knowledge of three-dimensional objects. First, a process of perceptual organization is used to form groupings and structures in the image that are likely to be invariant over a wide range of viewpoints. Second, a probabilistic ranking method is used to reduce the size of the search space during model based matching. Finally, a process of spatial correspondence brings the projections of three-dimensional models into direct correspondence with the image by solving for unknown viewpoint and model parameters. A high level of robustness in the presence of occlusion and missing data can be achieved through full application of a viewpoint consistency constraint. It is argued that similar mechanisms and constraints form the basis for recognition in human vision. This paper has been published in Artificial Intelligence, 31, 3 (March 1987), pp. 355–395. 1 1
