Deep learning-based 3D local feature descriptor from Mercator projections

M. Rezaei; M. Rezaeian; V. Derhami; F. Sohel; M. Bennamoun

doi:10.1016/j.cagd.2019.101771

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Deep learning-based 3D local feature descriptor from Mercator projections

Journal article

Open access

Peer reviewed

Deep learning-based 3D local feature descriptor from Mercator projections

M. Rezaei, M. Rezaeian, V. Derhami, F. Sohel and M. Bennamoun

Computer Aided Geometric Design, Vol.74

2019

DOI: https://doi.org/10.1016/j.cagd.2019.101771

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Abstract

Point clouds provide rich geometric information about a shape and a deep neural network can be used to learn effective and robust features. In this paper, we propose a novel local feature descriptor, which employs a Siamese network to directly learn robust features from the point clouds. We use a data representation based on the Mercator projection, then we use a Siamese network to map this projection into a 32-dimensional local descriptor. To validate the proposed method, we have compared it with seven state-of-the-art descriptor methods. Experimental results show the superiority of the proposed method compared to existing methods in terms of descriptiveness and robustness against noise and varying mesh resolutions.

Details

Title: Deep learning-based 3D local feature descriptor from Mercator projections
Authors/Creators: M. Rezaei (Author/Creator)
M. Rezaeian (Author/Creator)
V. Derhami (Author/Creator)
F. Sohel (Author/Creator)
M. Bennamoun (Author/Creator)
Publication Details: Computer Aided Geometric Design, Vol.74
Publisher: Elsevier
Identifiers: 991005541631007891
Copyright: © 2019 Elsevier B.V.
Murdoch Affiliation: College of Science, Health, Engineering and Education
Language: English
Resource Type: Journal article

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Collaboration types: Domestic collaboration; International collaboration
Citation topics: 4 Electrical Engineering, Electronics & Computer Science; 4.116 Robotics; 4.116.133 Simultaneous Localization and Mapping
Web Of Science research areas: Computer Science, Software Engineering; Mathematics, Applied
ESI research areas: Computer Science