  There is an increasing interest in applying deep learning to 3D mesh
segmentation. We observe that 1) existing feature-based techniques are often
slow or sensitive to feature resizing, 2) there are minimal comparative studies
and 3) techniques often suffer from reproducibility issue. This study
contributes in two ways. First, we propose a novel convolutional neural network
(CNN) for mesh segmentation. It uses 1D data, filters and a multi-branch
architecture for separate training of multi-scale features. Together with a
novel way of computing conformal factor (CF), our technique clearly
out-performs existing work. Secondly, we publicly provide implementations of
several deep learning techniques, namely, neural networks (NNs), autoencoders
(AEs) and CNNs, whose architectures are at least two layers deep. The
significance of this study is that it proposes a robust form of CF, offers a
novel and accurate CNN technique, and a comprehensive study of several deep
learning techniques for baseline comparison.
