Take a sneak peek at the new NIST.gov and let us know what you think!
(Please note: some content may not be complete on the beta site.).

View the beta site
NIST logo

Publication Citation: An Evaluation of Local Shape Descriptors for 3D Shape Retrieval

NIST Authors in Bold

Author(s): Sarah Y. Tang;
Title: An Evaluation of Local Shape Descriptors for 3D Shape Retrieval
Published: March 08, 2012
Abstract: As the usage of 3D models increases, so does the importance of developing accurate 3D shape retrieval algorithms. Most prominently, shape descriptors are used to describe the geometric and topological properties of objects and compared to determine two objects‰ similarity. They are split into two categories ‹ global and local. As local descriptors are in general more invariant to rotation, translation, and scaling, and can additionally be applied to articulated models and partial matching problems, many have been proposed. However, these descriptors are often evaluated independently and on different datasets, making them difficult to compare. Using the SHREC 2011 Shape Retrieval Contest of Non-rigid 3D Watertight Meshes dataset, we systematically evaluate a collection of local shape descriptors. We choose to apply them to the bag-of-words paradigm, where each object is represented as a histogram counting occurrences of each word of a visual dictionary. In addition, the role of vocabulary size and number of sample points taken from each object in performance is assessed. Salient point detection methods are applied to determine if the number of sample points can be decreased without sacrificing accuracy. Finally, information from two local descriptors is combined in a number of ways and changes in performance are investigated.
Citation: NIST Interagency/Internal Report (NISTIR) - 7812
Pages: 28 pp.
Keywords: 3D shape retrieval, local features, bag-of-words, evaluation
Research Areas: Data and Informatics, Data Mining, Scientific Computing, Imaging, Math
PDF version: PDF Document Click here to retrieve PDF version of paper (568KB)