An Indexing Scheme and Descriptor for 3D Object Retrieval Based on Local Shape Querying
Computer Vision and Pattern Recognition
2021-07-09 v1
Abstract
A binary descriptor indexing scheme based on Hamming distance called the Hamming tree for local shape queries is presented. A new binary clutter resistant descriptor named Quick Intersection Count Change Image (QUICCI) is also introduced. This local shape descriptor is extremely small and fast to compare. Additionally, a novel distance function called Weighted Hamming applicable to QUICCI images is proposed for retrieval applications. The effectiveness of the indexing scheme and QUICCI is demonstrated on 828 million QUICCI images derived from the SHREC2017 dataset, while the clutter resistance of QUICCI is shown using the clutterbox experiment.
Keywords
Cite
@article{arxiv.2008.02916,
title = {An Indexing Scheme and Descriptor for 3D Object Retrieval Based on Local Shape Querying},
author = {Bart Iver van Blokland and Theoharis Theoharis},
journal= {arXiv preprint arXiv:2008.02916},
year = {2021}
}
Comments
13 pages, 13 figures, to be published in a Special Issue in Computers & Graphics