3-D Object Recognition via Aspect Graph Aware 3-D Object Representation

This letter addresses the problem of 3-D object recognition, whose aim is to recognize and estimate the pose of user-defined 3-D object when given an image. One difficult problem for 3-D object recognition is false correspondences between input image and 3-D model. To overcome this problem, we propose a novel aspect graph aware 3-D object representation method which enable us to output continuous pose and deal with self-occlusion problem. We also propose a two-stage 2-D to 3-D false correspondence filter based on proposed 3-D representation to achieve more consistent 2-D to 3-D matching pairs. We evaluate our proposed algorithm on Weizman Cars Viewpoint dataset and it demonstrates obvious improvement on localization and pose estimation accuracy compared with traditional methods. Besides, our proposed method accelerates computation time.