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

Rezaei, M., Rezaeian, M., Derhami, V., Sohel, F. and Bennamoun, M. (2019) Deep learning-based 3D local feature descriptor from Mercator projections. Computer Aided Geometric Design, 74 .

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Embargoed until August 2021.

Link to Published Version: 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.

Item Type: Journal Article
Murdoch Affiliation: College of Science, Health, Engineering and Education
Publisher: Elsevier
Copyright: © 2019 Elsevier B.V.
URI: http://researchrepository.murdoch.edu.au/id/eprint/50819
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