Analysis of face and segment level descriptors for robust 3D co-segmentation

3D shape co-segmentation is an important topic in computer graphics. The idea of co-analysis brings new insights into understanding a collection of shapes. Rather than analysing individual shapes, an entire set is looked at, giving much more information about the class of shape in the set. Existing co-segmentation techniques use both face and segment level descriptors in order to provide enough information to give an accurate co-segmentation result. In the literature, a lot of these descriptors are proposed but there is limited empirical studies to compare which would perform well. In this paper, we have two aims: (a) propose new useful face and segment level descriptors and (b) analyse the effectiveness of them. Our experiment indicates that smoothly varying descriptors (Average Euclidean Distance) that respects geometry would improve the segmentation results.