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

Masoumeh Rezaei, Mehdi Rezaeian, Vali Derhami, Ferdous Sohel, Mohammed Bennamoun

Research output: Contribution to journalArticlepeer-review

6 Citations (Scopus)


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.
Original languageEnglish
Article number101771
JournalComputer Aided Geometric Design
Publication statusPublished - Oct 2019


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