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Excerpt from Robotics Research Technical Report: Three-Dimensional Object Recognition From Single Two-Dimensional Images Much recent research in computer vision has been aimed at the reconstruction of depth information from the two-dimensional visual input. An assumption underlying some of this research is that the recognition of three-dimensional objects can most easily be carried out by matching against reconstructed three-dimensional data. However, there is reason to believe that this is not the primary pathway used for recognition in human vision and that most practical applications of computer vision could similarly be performed without bottom-up depth reconstruction. Although depth measurement has an important role in certain visual problems, it is often unavailable or is expensive to obtain. General-purpose vision must be able to function effectively even in the absence of the extensive information required for bottom-up reconstruction of depth or other physical properties. In fact, humanvision does function very well at recognizing images, such as simple line drawings, that lack any reliable clues for the reconstruction of depth prior to recognition. This capability also parallels many other areas in which human vision can make use of partial and locally ambiguous information to achieve reliable identifications. This paper presents several methods for bridging the gap between two - dimensional images and knowledge of three dimensional objects without any preliminary derivation of depth. Of equal importance, these methods address the critical problem of robustness, with the ability to function in spite of missing data, occlusion, and many forms of image degradation. How is it possible to recognize an object from its two-dimensional projection when we have no prior knowledge of the viewpoint from which we will be seeing it? An important role is played by the process of perceptual organization, which detects groupings and structures in the image that are