A 3D Model Retrieval Based on Combinations of Partial Shape Descriptors

Abstract

Similarity retrieval techniques for 3D models have been intensively investigated the last few years. The purpose has been to improve precision of the similarity retrievals, and as a result various types of shape descriptors have been proposed. Several shape descriptors use the bounding box of a 3D model during a shape descriptor extraction process, and computation of the bounding box is important for accurately identifying shape descriptors. In our previous shape descriptor extraction approaches, only one bounding box was used for each 3D model. However, use of one bounding box is a very rough approximation of the shape for certain 3D models. When the bounding box becomes very sparse for certain targeted 3D models, the approach can not compute shape descriptors accurately. In this research, we have extended the shape descriptor computation technique by using a multiple number of bounding boxes. 3D models are decomposed into multiple parts, and multiple numbers of bounding boxes are used for each decomposed part. Shape descriptors are extracted from each decomposed 3D model part independently, and they are combined with weighted values based on the proportions of area size of each decomposed part. Our preliminary experiments showed that similarity retrievals results were improved for certain 3D models by using a combination of partial shape descriptors.