Multiscale bilateral filtering to detect 3D interest points

Abstract

The detection of 3D interest points is a central problem in computer graphics, computer vision, and pattern recognition. It is also an important preprocessing step in the analysis of 3D model matching. Although studied for decades, detecting 3D interest points remains a challenge. In this study, a novel multiscale bilateral filtering method is presented to detect 3D interest points. This method first simplifies repeatedly the input 3D mesh to form k multiresolution meshes. For each mesh, on the basis of the computed saliency of the mesh vertex, the bilateral filtering is used to remove the noise of the mesh saliencies and the global contrast to normalise the saliencies, and then the interest points are extracted on the basis of the normalised saliency. The proposed method then gathers and clusters all interest points detected on the k multiresolution meshes, and the centres of these clusters are treated as the final interest points. In this method, both the spatial closeness and the geometric similarities of the mesh vertices are considered during the bilateral filtering process. The experimental results validate the effectiveness of the proposed method to detect 3D interest points. This method is also tested the potential to distinguish 3D models.