Detecting the pose of rigid objects: A comparison of paradigms

The geometric constraints available in the structure of rigid objects can easily be exploited by matching algorithms for both object recog-nition and localization. Two matching paradigms; --pose clustering, hypothesize and test are discussed and compared with respect to accuracy, computational cost, and operation in noisy and multiple object environments. Each paradigm offers certain relative advantages and implies a certain computer architecture. All algorithms in each category ultimately depend on adequate detection of primitive features and may encounter large increases in computation time in going from a single object to a multiple object environment.