An evaluation of local shape descriptors for 3D shape retrieval

TL;DR

This paper systematically evaluates various local shape descriptors for 3D shape retrieval using the SHREC 2011 dataset, analyzing their performance, sampling methods, and descriptor combinations to improve retrieval accuracy.

Contribution

It provides a comprehensive comparison of local shape descriptors, sampling strategies, and descriptor fusion methods in 3D shape retrieval, which was lacking in prior work.

Findings

Certain descriptors outperform others in retrieval accuracy

Salient point detection improves descriptor effectiveness

Combining descriptors can enhance retrieval performance

Abstract

As the usage of 3D models increases, so does the importance of developing accurate 3D shape retrieval algorithms. A common approach is to calculate a shape descriptor for each object, which can then be compared to determine two objects' similarity. However, these descriptors are often evaluated independently and on different datasets, making them difficult to compare. Using the SHREC 2011 Shape Retrieval Contest of Non-rigid 3D Watertight Meshes dataset, we systematically evaluate a collection of local shape descriptors. We apply each descriptor to the bag-of-words paradigm and assess the effects of varying the dictionary's size and the number of sample points. In addition, several salient point detection methods are used to choose sample points; these methods are compared to each other and to random selection. Finally, information from two local descriptors is combined in two ways and changes in performance are investigated. This paper presents results of these experiment