Using Spin Images for Efficient Object Recognition in Cluttered 3D Scenes
IEEE Transactions on Pattern Analysis and Machine Intelligence
A Survey of Content Based 3D Shape Retrieval Methods
SMI '04 Proceedings of the Shape Modeling International 2004
Discovering Objects and their Localization in Images
ICCV '05 Proceedings of the Tenth IEEE International Conference on Computer Vision (ICCV'05) Volume 1 - Volume 01
Shape Topics: A Compact Representation and New Algorithms for 3D Partial Shape Retrieval
CVPR '06 Proceedings of the 2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition - Volume 2
Foundations and Trends® in Computer Graphics and Vision
A Bag of Words Approach for 3D Object Categorization
MIRAGE '09 Proceedings of the 4th International Conference on Computer Vision/Computer Graphics CollaborationTechniques
Three-dimensional shape searching: state-of-the-art review and future trends
Computer-Aided Design
Unique signatures of histograms for local surface description
ECCV'10 Proceedings of the 11th European conference on computer vision conference on Computer vision: Part III
Performance Evaluation of 3D Keypoint Detectors
International Journal of Computer Vision
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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.