Recognition of Free-Form Objects in Dense Range Data Using Local Features

Abstract

Describes a system for recognizing free-form 3D objects in dense range data employing local features and object-centered geometric models. Local features are extracted from range images and object models using curvature analysis, and variability in feature size is accommodated by decomposition of features into sub-features. Shape indices and other attributes provide a basis for correspondence between compatible image and model features and subfeatures, as well as pruning of invalid correspondences. A verification step provides a final ranking of object identity and pose hypotheses. The evaluation system contained 10 free-form objects and was tested using 10 range images with two objects from the database in each image. Comments address strengths of the proposed technique as well as areas for future improvement.