  We present a semi-supervised co-analysis method for learning 3D shape styles
from projected feature lines, achieving style patch localization with only weak
supervision. Given a collection of 3D shapes spanning multiple object
categories and styles, we perform style co-analysis over projected feature
lines of each 3D shape and then backproject the learned style features onto the
3D shapes. Our core analysis pipeline starts with mid-level patch sampling and
pre-selection of candidate style patches. Projective features are then encoded
via patch convolution. Multi-view feature integration and style clustering are
carried out under the framework of partially shared latent factor (PSLF)
learning, a multi-view feature learning scheme. PSLF achieves effective
multi-view feature fusion by distilling and exploiting consistent and
complementary feature information from multiple views, while also selecting
style patches from the candidates. Our style analysis approach supports both
unsupervised and semi-supervised analysis. For the latter, our method accepts
both user-specified shape labels and style-ranked triplets as clustering
constraints.We demonstrate results from 3D shape style analysis and patch
localization as well as improvements over state-of-the-art methods. We also
present several applications enabled by our style analysis.
