Surface Patch Detection of 3D Point Cloud Using Local Shape Descriptor

Visual saliency is determined through the perceptual information that enables to detect interesting regions in the query 3D models, which attracts human visual attention. Local descriptors are common and externally effective for various 3D tasks such as registration, object tracking object recognition and saliency detection. In this paper, we present a suitable solution of saliency regions-based surface patch detection in 3D point cloud images using pairwise 2D histograms of local descriptors constructed from both global and local geometry information. The local information is estimated based on the local descriptor formulated from the relation between 3D point and its neighboring points (neighbourhood points-based descriptor). The global information is estimated by extrusion shape descriptor based radial map construction. The proposed method has three well-defined steps for preprocessing workflow in order to prepare data points for salient region detection process composed of downsampling-outliers removal, normal vector computation for each point and neighbourhood determination for each point. From the experiments, the local shape descriptors adopted in this paper are reliable to detect more salient points-based surface patch detection in the 3D point cloud model. The experiments on a publicly point cloud database were attained high accuracy of the proposed surface patch detector.