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

3D Mesh Segmentation via Multi-branch 1D Convolutional Neural Networks