Efficient Recognition of Highly Similar 3D Objects in Range Images

Most existing work in 3D object recognition in computer vision has been on recognizing dissimilar objects using a small database. For rapid indexing and recognition of highly similar objects, this paper proposes a novel method which combines the feature embedding for the fast retrieval of surface descriptors, novel similarity measures for correspondence and a support vector machine (SVM)-based learning technique for ranking the hypotheses. The local surface patch (LSP) representation is used to find the correspondences between a model-test pair. Due to its high dimensionality, an embedding algorithm is used that maps the feature vectors to a low-dimensional space where distance relationships are preserved. By searching the nearest neighbors in low dimensions, the similarity between a model-test pair is computed using the novel features. The similarities for all model-test pairs are ranked using the learning algorithm to generate a short list of candidate models for verification. The verification is performed by aligning a model with the test object. The experimental results, on the UND dataset (302 subjects with 604 images) and the UCR dataset (155 subjects with 902 images) that contain 3D human ears, are presented and compared with the geometric hashing technique to demonstrate the efficiency and effectiveness of the proposed approach.