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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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 |
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Murdoch Affiliation(s): | 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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