Abstract
In this paper, we study the problem of semantic annotation on 3D models that are represented as shape graphs. A
functional view is taken to represent localized information
on graphs, so that annotations such as part segment or
keypoint are nothing but 0-1 indicator vertex functions.
Compared with images that are 2D grids, shape graphs are
irregular and non-isomorphic data structures. To enable
the prediction of vertex functions on them by convolutional
neural networks, we resort to spectral CNN method that enables weight sharing by parametrizing kernels in the spectral domain spanned by graph Laplacian eigenbases. Under this setting, our network, named SyncSpecCNN, strives
to overcome two key challenges: how to share coefficients
and conduct multi-scale analysis in different parts of the
graph for a single shape, and how to share information
across related but different shapes that may be represented
by very different graphs. Towards these goals, we introduce
a spectral parametrization of dilated convolutional kernels
and a spectral transformer network. Experimentally we
tested SyncSpecCNN on various tasks, including 3D shape
part segmentation and keypoint prediction. State-of-the-art
performance has been achieved on all benchmark datasets.