Partial 3D Object Retrieval using Local Binary QUICCI Descriptors and Dissimilarity Tree Indexing

TL;DR

This paper introduces a new pipeline for partial 3D object retrieval that combines modified binary local descriptors with a novel indexing structure, achieving high accuracy and efficiency on a standard dataset.

Contribution

It presents a modified QUICCI descriptor suitable for partial retrieval and a Dissimilarity Tree index that accelerates large-scale binary descriptor searches.

Findings

Achieved near-ideal retrieval results on SHREC'16 dataset.

Significantly accelerated search times with the Dissimilarity Tree.

Applicable to various binary descriptors beyond QUICCI.

Abstract

A complete pipeline is presented for accurate and efficient partial 3D object retrieval based on Quick Intersection Count Change Image (QUICCI) binary local descriptors and a novel indexing tree. It is shown how a modification to the QUICCI query descriptor makes it ideal for partial retrieval. An indexing structure called Dissimilarity Tree is proposed which can significantly accelerate searching the large space of local descriptors; this is applicable to QUICCI and other binary descriptors. The index exploits the distribution of bits within descriptors for efficient retrieval. The retrieval pipeline is tested on the artificial part of SHREC'16 dataset with near-ideal retrieval results.