Pattern Recognition ( IF 7.196 ) Pub Date : 2019-12-25 , DOI: 10.1016/j.patcog.2019.107186 Sheng Ao; Yulan Guo; Jindong Tian; Yong Tian; Dong Li
Local reference frames (LRFs) have been widely used for 3D local surface description. In this work, we propose a repeatable LRF with strong robustness to different nuisances. Different from existing LRF methods, the proposed LRF uses a part of neighboring points within the support region to calculate the z-axis, and performs an effective feature transformation on the neighboring points to define the x-axis. Specifically, feature transformation is applied to the data on a projection plane based on three point distribution characteristics via weighted strategies. These characteristics include the z-height, the distance to the center and the average length to 1-ring neighbors, covariance analysis is then applied to the transformed points to obtain the eigenvector with the largest eigenvalue, which points towards the maximum variance direction. Using a sign disambiguation technique, the modified eigenvector is used to define the final x-axis. Furthermore, a scale strategy is proposed to improve the robustness of the LRF with respect to mesh decimation. The proposed LRF was rigorously tested on six public benchmark datasets consisting of three different application contexts, i.e., 3D shape retrieval, 3D object recognition and registration. Experiments show that our method achieves significantly higher repeatability and stronger robustness than the state-of-the-arts under Gaussian noise, shot noise and mesh resolution variation. Finally, the descriptor matching results on four typical datasets further demonstrate the effectiveness of our LRF.