Abstract
Model-based object recognition systems have rarely dealt directly with 3D perspective while matching models to images. The algorithms presented here use 3D pose recovery during matching to explicitly and quantitatively account for changes in model appearance associated with 3D perspective. These algorithms use random-start local search to find, with high probability, the globally optimal correspondence between model and image features in spaces containing over 2100 possible matches. Three specific algorithms are compared on robot landmark recognition problems. A full-perspective algorithm uses the 3D pose algorithm in all stages of search while two hybrid algorithms use a computationally less demanding weak-perspective procedure to rank alternative matches and updates 3D pose only when moving to a new match. These hybrids successfully solve problems involving perspective, and in less time than required by the full-perspective algorithm.
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