Multi-feature 3D model retrieval using view-based techniques

Most research on 3D model retrieval focus on shape as the main feature used for search and retrieval. In this paper, we explore augmenting 3D shape-based similarity measures by combining shape with color features. There are already numerous techniques for content-based 3D model retrieval utilizing shape, some use global shape features, shape distributions, or view-based methods. This paper uses a view-based approach using 2D projections of 3D models taken from different viewpoints. The main problem with view-based techniques is how to normalize the rotation of the mesh in 3D space to properly align the model to the projection planes. In our work, we use Normal-vectors PCA (NPCA). The 3D model is first projected onto three planes using their normal vectors and gray-level images (inner elevations) that describe the depth information are generated. The angular radial transform (ART) from MPEG-7 is then used on these inner elevations to extract a feature descriptor. These descriptors are called the inner elevation descriptor (IED) and are used to match the query with the database object. This shape feature is then augmented using color histograms considering perceptual color similarity. It is then tested on the Princeton Shape Benchmark and compared to well-known approaches.