Nonrigid 3D shape retrieval with HAPPSA novel hybrid augmented point pair signature

Authors Organisations
Type Conference Proceeding (Non-Journal item)
Original languageEnglish
Title of host publicationProceedings - 6th Annual Conference on Computational Science and Computational Intelligence, CSCI 2019
PublisherIEEE Press
Pages662-668
Number of pages7
ISBN (Electronic)9781728155845
ISBN (Print)9781728155852
DOI
Publication statusPublished - 20 Apr 2020
Event6th Annual International Conference on Computational Science and Computational Intelligence, CSCI 2019 - Las Vegas, United States of America
Duration: 05 Dec 201907 Dec 2019

Publication series

NameProceedings - 6th Annual Conference on Computational Science and Computational Intelligence, CSCI 2019

Conference

Conference6th Annual International Conference on Computational Science and Computational Intelligence, CSCI 2019
CountryUnited States of America
CityLas Vegas
Period05 Dec 201907 Dec 2019
Links
Handle.net
View graph of relations
Citation formats

Abstract

A robust, yet computationally efficient signature for describing 3D shape remains a challenge for 3D computer vision and related applications. Having a signature that is generalizable across a wider range of datasets becomes another important research issue. This paper proposes a novel Hybrid signature, the Augmented Point Pair Signature (HAPPS), that is robust, highly discriminating, efficient, and capable of effectively representing 3D point cloud and polygon mesh surfaces. We tested the overall performances of HAPPS on three standardized benchmark datasets for 3D shape retrieval: The Shape Retrieval Contest 2018 (SHREC'18) protein shapes benchmark, with 2,267 protein conformers, SHREC'17 Point cloud Retrieval of Nonrigid Toys (PRoNTo), with 100 3D point clouds, and SHREC'10 Nonrigid shape retrieval having 200 triangular meshes. Using 6 standard retrieval performance metrics to evaluate our results, we demonstrated the superiority of our HAPPS retrieval method over several other state-of-the-art methods for the SHREC'18 protein dataset, while also competing side-by-side with the best 2 performing methods for the other benchmark datasets.

Keywords

  • 3D shape analysis, Content-based retrieval, Local/Global descriptor, Shapes descriptors