Co-Segmentation of 3D Shapes via Subspace Clustering

dc.contributor.authorHu, Ruizhenen_US
dc.contributor.authorFan, Lubinen_US
dc.contributor.authorLiu, Ligangen_US
dc.contributor.editorEitan Grinspun and Niloy Mitraen_US
dc.description.abstractWe present a novel algorithm for automatically co-segmenting a set of shapes from a common family into consistent parts. Starting from over-segmentations of shapes, our approach generates the segmentations by grouping the primitive patches of the shapes directly and obtains their correspondences simultaneously. The core of the algorithm is to compute an affinity matrix where each entry encodes the similarity between two patches, which is measured based on the geometric features of patches. Instead of concatenating the different features into one feature descriptor, we formulate co-segmentation into a subspace clustering problem in multiple feature spaces. Specifically, to fuse multiple features, we propose a new formulation of optimization with a consistent penalty, which facilitates both the identification of most similar patches and selection of master features for two similar patches. Therefore the affinity matrices for various features are sparsity-consistent and the similarity between a pair of patches may be determined by part of (instead of all) features. Experimental results have shown how our algorithm jointly extracts consistent parts across the collection in a good manner.en_US
dc.description.seriesinformationComputer Graphics Forumen_US
dc.publisherThe Eurographics Association and Blackwell Publishing Ltd.en_US
dc.subjectI.4.6 [Computer Graphics]en_US
dc.titleCo-Segmentation of 3D Shapes via Subspace Clusteringen_US