Work

An indexing scheme and descriptor for 3D object retrieval based on local shape querying

Year: 2020

Type: article

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.

Source: Computers & Graphics

DOI: 10.1016/j.cag.2020.09.001

Authors: Bart Iver van Blokland, Theoharis Theoharis

Institution: Norwegian University of Science and Technology

Language: English

Topic: 3D Shape Modeling and Analysis

Subfield: Computational Mechanics

Field: Engineering

Domain: Physical Sciences

Open Access status: hybrid

Estimated APC paid: $2,770

Funders: Nvidia, Norges Teknisk-Naturvitenskapelige Universitet