SHape REtrieval contest 2008: Generic models

Abstract

The first of the SHREC series of 3D model retrieval contests, SHREC 2006 [5] organized by Prof. Veltkamp et al. has made an impact in the way researchers compare performances of their 3D model retrieval methods. The task was to retrieve polygon soup models found in the Princeton Shape Benchmark database [5] having diverse shape and semantics. While many researchers used the SHREC 2006 as their benchmark, there has been no “official” contest since 2006 that used the same SHREC 2006 format but with up-to-date algorithms and methods. The SHREC 2007 added new tracks, e.g., for 3D face models, watertight models, protein models, CAD models, partial matching, and relevance feedback. However, the format of SHREC 2006 was missing. This SHREC 2008 Generic Models Track (GMT) tries to repeat the SHREC 2006 so that we can compare state-of-the-art methods for polygon soup models by using a stable benchmark dataset and ground truth classifications. A change from the SHREC 2006 to the SHREC 2008 GMT is the acknowledgement of learning based algorithms for 3D model retrieval. The SHREC 2008 GMT has two entry categories depending on if supervised learning is used or not. We wanted to encourage various forms of learning algorithms, as we believe learning algorithms are as essential as features themselves for effective 3D model retrieval. At the same time, we do not want to discourage methods without supervised learning. So we created two sub-tracks, one for unsupervised methods and the other for supervised methods. To test the behavior of supervised method for the queries having “unseen” ground truth classifications, we added a new set of queries, in addition to the original set of queries used in the SHREC 2006.