3D model retrieval using global and local radial distances

TL;DR

This paper introduces a hybrid 3D shape descriptor combining global and local radial distances, improving retrieval accuracy over traditional histogram and view-based methods.

Contribution

The paper proposes a novel hybrid shape descriptor that integrates global and local radial distance features for enhanced 3D model retrieval.

Findings

Hybrid descriptor outperforms traditional methods

Global and local features complement each other

Improved retrieval accuracy demonstrated

Abstract

3D model retrieval techniques can be classified as histogram-based, view-based and graph-based approaches. We propose a hybrid shape descriptor which combines the global and local radial distance features by utilizing the histogram-based and view-based approaches respectively. We define an area-weighted global radial distance with respect to the center of the bounding sphere of the model and encode its distribution into a 2D histogram as the global radial distance shape descriptor. We then uniformly divide the bounding cube of a 3D model into a set of small cubes and define their centers as local centers. Then, we compute the local radial distance of a point based on the nearest local center. By sparsely sampling a set of views and encoding the local radial distance feature on the rendered views by color coding, we extract the local radial distance shape descriptor. Based on these two shape descriptors, we develop a hybrid radial distance shape descriptor for 3D model retrieval. Experiment results show that our hybrid shape descriptor outperforms several typical histogram-based and view-based approaches.