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3D mesh segmentation via multi-branch 1D convolutional neural networks / David George; Xianghua Xie; Gary KL Tam

Graphical Models, Volume: 96, Pages: 1 - 10

Swansea University Author: Xie, Xianghua

  • Accepted Manuscript under embargo until: 30th January 2019

Abstract

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. Secondl...

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Published in: Graphical Models
ISSN: 15240703
Published: Elsevier 2018
Online Access: Check full text

URI: https://cronfa.swan.ac.uk/Record/cronfa39386
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Abstract: 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.
Keywords: Deep learning, deep neural network, efficient learning, mesh processing, mesh segmentation.
College: College of Science
Start Page: 1
End Page: 10