Pose determination for an object in a 3-D image using geometric hashing and the interpretation tree

A model-based pose determination algorithm for segmented 3-D images has been developed using the geometric hashing method and the interpretation tree. Its major advantage is an intensive off-line model preprocessing stage, where model information is indexed into a hash-table using minimal, transformation invariant features. This enables the on-line recognition algorithm, with its voting procedure, to be particularly efficient. Once the correspondence of a minimal set of features is established by the geometric hashing method, the interpretation tree is used to obtain the complete set of correspondences between the object´s model and the segmented range image. The best pose estimate is then found using a constrained least-squares method