We desgin a novel fully convolutional network architecture for shapes,
denoted by Shape Fully Convolutional Networks (SFCN). 3D shapes are represented
as graph structures in the SFCN architecture, based on novel graph convolution
and pooling operations, which are similar to convolution and pooling operations
used on images. Meanwhile, to build our SFCN architecture in the original image
segmentation fully convolutional network (FCN) architecture, we also design and
implement a generating operation} with bridging function. This ensures that the
convolution and pooling operation we have designed can be successfully applied
in the original FCN architecture. In this paper, we also present a new shape
segmentation approach based on SFCN. Furthermore, we allow more general and
challenging input, such as mixed datasets of different categories of shapes}
which can prove the ability of our generalisation. In our approach, SFCNs are
trained triangles-to-triangles by using three low-level geometric features as
input. Finally, the feature voting-based multi-label graph cuts is adopted to
optimise the segmentation results obtained by SFCN prediction. The experiment
results show that our method can effectively learn and predict mixed shape
datasets of either similar or different characteristics, and achieve excellent
segmentation results.

本文提出一种新的形状全卷积网络 (Shape Fully Convolutional Networks，SFCN) 架构，用于三维形状的分割，该架构基于图形卷积和池化操作。同时，基于 SFCN 架构，还提出了一种新的形状分割方法，并采用基于特征投票的多标签图割算法来优化 SFCN 预测得到的分割结果。实验结果表明，我们的方法在学习和预测相似或不同特征的混合形状数据集方面表现出很好的效果。