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 parameterizing kernels in the spectral domain spanned by
graph laplacian eigenbases. Under this setting, our network, named SyncSpecCNN,
strive 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 parameterization of dilated convolutional kernels and a spectral
transformer network. Experimentally we tested our SyncSpecCNN on various tasks,
including 3D shape part segmentation and 3D keypoint prediction.
State-of-the-art performance has been achieved on all benchmark datasets.

本文研究了基于图形表示的三维模型的语义注释问题，使用基于谱 CNN 的方法解决了权重共享和跨不同形状共享信息的挑战，实验表明在三维形状部件分割和关键点预测等任务中，取得了最先进的性能。