Object recognition in dense range images using a CAD system as a model base

A model-based vision system is proposed in which a commercial CAD system has been used for object modeling. Assuming that the model is known, the corresponding object in the scene is located. Given the CAD model of an object, certain features of the model are extracted, while others are precalculated and stored. The given dense 3-D range image is segmented into a set of homogeneous surface patches using a segmentation procedure. Properties such as curvature, surface normal, and surface area are approximated for each surface patch. For each extracted surface patch, three filters are applied to the previously obtained model features to find the best match. Then, a global consistency filter is applied to remove ambiguities and to find the best matched model. >