A novel approach for 3D model recognition based on SSCD

In this paper, a 3D model recognition method based on modification of Spatial Structure Circular Descriptor (SSCD) is proposed. Firstly, the model optimization process removes all valueless points. Secondly, the SSCD descriptor to get a spatial value is used. This method uses a Spherical Grade Projection (SGP) to project points on a plane. However, in our case the SGP is not used. Our proposed method is based on extraction of spatial information, which is projected to image. To store the full spatial information of 3D model to an image spherical transformation is used. Mostly, low-polygon models have small amount of points, which are projected to sphere hence, is created empty spaces. To avoid this problem is used a convolution with gradient function. Finally, we calculate the similarities between dataset of 3D models. The algorithm has been tested on 100 different 3D models (10 models for each class). The experimental result shows that the proposed method has a positive effect on overall recognition performance and outperforms other examined methods.