The goal of this work is to provide data for quantitative analysis of how people consistently segment a set of shapes and for evaluation of our active co-analysis algorithm. To build the dataset, we have collected 11 sets of shapes which possess a consistent
ground-truth segmentation and labeling. Among them, seven sets from the dataset of Sidi et al. [2011]. Since we consider the labeling of large sets as one of the main motivations of our work, we created three additional large sets: tele-alines, vases and chairs.
These three sets consists of 200, 300, 400 shapes, respetively. We also created a small but challenging set of irons.
Sources
The related projects from which the dataset grew to its current form are:
Yunhai Wang, Shmulik Asafi, Oliver van Kaick, Hao Zhang, Daniel Cohen-Or, Baoquan Chen, Active Co-Analysis of a Set of Shapes, ACM Transactions on Graphics (Proc. SIGGRAPH Asia), vol. 31, n. 6, 2012.
Oana Sidi, Oliver van Kaick, Yanir Kleiman, Hao Zhang, Daniel Cohen-Or, Unsupervised Co-Segmentation of a Set of Shapes via Descriptor-Space Spectral Clustering, ACM Transactions on Graphics (Proc. SIGGRAPH Asia), vol. 30, n. 6, pp. 126-134, 2011
Oliver van Kaick, Andrea Tagliasacchi, Oana Sidi, Hao Zhang, Daniel Cohen-Or, Lior Wolf, Ghassan Hamarneh, Prior Knowledge for Part Correspondence, Computer Graphics Forum (Proc. Eurographics), vol. 30, n. 2, pp. 553-562, 2011.
