Effective 2D/3D Registration using Curvilinear Saliency Features and Multi-Class SVM

Registering a single intensity image to a 3D geometric model represented by a set of depth images is still a challenge. Since depth images represent only the shape of the objects, in turn, the intensity image is relative to viewpoint, texture and lighting condition. Thus, it is essential to firstly bring 2D and 3D representations to common features and then match them to find the correct view. In this paper, we used the concept of curvilinear saliency, related to curvature estimation, for extracting the shape information of both modalities. However, matching the features extracted from an intensity image to thousand(s) of depth images rendered from a 3D model is an exhausting process. Consequently, we propose to cluster the depth images into groups based on Clustering Rule-based Algorithm (CRA). In order to reduce the matching space between the intensity and depth images, a 2D/3D registration framework based on multi-class Support Vector Machine (SVM) is then used. SVM predicts the closest class (i.e., a set of depth images) to the input image. Finally, the closest view is refined and verified by using RANSAC. The effectiveness of the proposed registration approach has been evaluated by using the public PASCAL3D+ dataset. The obtaining results show that the proposed algorithm provides a high precision with an average of 88%.