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

arXiv: https://arxiv.org/abs/1705.11050 · HTML: https://mlsanity.com/paper/1705.11050/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 31 May 2017.

## Built on by (most cited first, 3 of 3)

- [On Efficient Real-Time Semantic Segmentation: A Survey](https://mlsanity.com/paper/2206.08605/) (2022)
- [SAGA: Spectral Adversarial Geometric Attack on 3D Meshes](https://mlsanity.com/paper/2211.13775/) (2022)
- [A convolutional neural network for teeth margin detection on 3-dimensional dental meshes](https://mlsanity.com/paper/2107.03030/) (2021)

## Abstract (arXiv)

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.