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We present a semi-supervised co-analysis method for learning 3D shape stylesfrom projected feature lines, achieving style patch localization with only weaksupervision. Given a collection of 3D shapes spanning multiple objectcategories and styles, we perform style co-analysis over projected featurelines of each 3D shape and then backproject the learned style features onto the3D shapes. Our core analysis pipeline starts with mid-level patch sampling andpre-selection of candidate style patches. Projective features are then encodedvia patch convolution. Multi-view feature integration and style clustering arecarried out under the framework of partially shared latent factor (PSLF)learning, a multi-view feature learning scheme. PSLF achieves effectivemulti-view feature fusion by distilling and exploiting consistent andcomplementary feature information from multiple views, while also selectingstyle patches from the candidates. Our style analysis approach supports bothunsupervised and semi-supervised analysis. For the latter, our method acceptsboth user-specified shape labels and style-ranked triplets as clusteringconstraints.We demonstrate results from 3D shape style analysis and patchlocalization as well as improvements over state-of-the-art methods. We alsopresent several applications enabled by our style analysis.

Semi-Supervised Co-Analysis of 3D Shape Styles from Projected Lines