New Method for 3D Shape Retrieval

TL;DR

This paper introduces a novel 3D shape retrieval method utilizing characteristic level images and Hu moments for indexing, combined with Hausdorff distance for similarity measurement, evaluated on the NTU database.

Contribution

The paper presents a new 3D object recognition and retrieval technique based on binary images and Hu moments, offering an alternative to existing methods.

Findings

Effective retrieval performance on NTU database

Utilizes characteristic level images for 3D shape representation

Employs Hausdorff distance for accurate similarity measurement

Abstract

The recent technological progress in acquisition, modeling and processing of 3D data leads to the proliferation of a large number of 3D objects databases. Consequently, the techniques used for content based 3D retrieval has become necessary. In this paper, we introduce a new method for 3D objects recognition and retrieval by using a set of binary images CLI (Characteristic level images). We propose a 3D indexing and search approach based on the similarity between characteristic level images using Hu moments for it indexing. To measure the similarity between 3D objects we compute the Hausdorff distance between a vectors descriptor. The performance of this new approach is evaluated at set of 3D object of well known database, is NTU (National Taiwan University) database.