  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.
