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Diffusion geometry derived keypoints and local descriptors for 3D deformable shape analysis

Wang, X., Bennamoun, M., Sohel, F. and Lei, H. (2020) Diffusion geometry derived keypoints and local descriptors for 3D deformable shape analysis. Journal of Circuits, Systems and Computers . Art. 2150016.

Link to Published Version: https://doi.org/10.1142/S021812662150016X
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Abstract

Geometric analysis of three-dimensional (3D) surfaces with local deformations is a challenging task, required by mobile devices. In this paper, we propose a new local feature-based method derived from diffusion geometry, including a keypoint detector named persistence-based Heat Kernel Signature (pHKS), and a feature descriptor named Heat Propagation Strips (HeaPS). The pHKS detector first constructs a scalar field using the heat kernel signature function. The scalar field is generated at a small scale to capture fine geometric information of the local surface. Persistent homology is then computed to extract all the local maxima from the scalar field, and to provide a measure of persistence. Points with a high persistence are selected as pHKS keypoints. In order to describe a keypoint, an intrinsic support region is generated by the diffusion area. This support region is more robust than its geodesic distance counterpart, and provides a local surface with adaptive scale for subsequent feature description. The HeaPS descriptor is then developed by encoding the information contained in both the spatial and temporal domains of the heat kernel. We conducted several experiments to evaluate the effectiveness of the proposed method. On the TOSCA Dataset, the HeaPS descriptor achieved a high performance in terms of descriptiveness. The feature detector and descriptor were then tested on the SHREC 2010 Feature Detection and Description Dataset, and produced results that were better than the state-of-the-art methods. Finally, their application to shape retrieval was evaluated. The proposed pHKS detector and HeaPS descriptor achieved a notable improvement on the SHREC 2014 Human Dataset.

Item Type: Journal Article
Murdoch Affiliation: School of Engineering and Information Technology
Publisher: World Scientific Publishing Co.
Copyright: © 2020 World Scientific Publishing Co Pte Ltd
URI: http://researchrepository.murdoch.edu.au/id/eprint/58250
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