On the Use of Implicit Shape Models for Recognition of Object Categories in 3D Data

The ability of recognizing object categories in 3D data is still an underdeveloped topic. This paper investigates on adopting Implicit Shape Models (ISMs) for 3D categorization, that, differently from current approaches, include also information on the geometrical structure of each object category. ISMs have been originally proposed for recognition and localization of categories in cluttered images. Modifications to allow for a correct deployment for 3D data are discussed. Moreover, we propose modifications to three design points within the structure of a standard ISM to enhance its effectiveness for the categorization of databases entries, either 3D or 2D: namely, codebook size and composition, codeword activation strategy and vote weight strategy. Experimental results on two standard 3D datasets allow us to discuss the positive impact of the proposed modifications as well as to show the performance in recognition accuracy yielded by our approach compared to the state of the art.