Model-based 3D object recognition using intensity and range images

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Abstract
This paper describes a model based vision system in which a commercial 3-D computer graphics system has been used for object modeling and visual clue generation. Given the computer generated model image (i.e., color, depth, ...) a conventional CCD camera image and the corresponding scanned 3-D dense range map of the real scene, the object can be located in it. Our system, called three dimensional model based approach (3D-MBA), uses image pyramid of resolution and prediction-verification processes. To optimize the object recognition scheme, it first forms a set of hypotheses about the objects present in the scene and then proceeds by trying to confirm/reject them. If any part of the object hypothesis is missing, the system uses the object model to predict the shape, location, and orientation of that missing part. This paper focuses on how this is done using newly developed segmentation algorithms extracting `regions of interest' from range images (depth map) of the scene. Illustrative examples of object recognition in simple and complex scenes are presented.

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