English

Abstract

Local surface description is a critical stage for surface matching. This paper presents a highly distinctive local surface descriptor, namely TriSI. From a keypoint, we first construct a unique and repeatable local reference frame (LRF) using all the points lying on the local surface. We then generate three spin images from the three coordinate axes of the LRF. These spin images are concatenated and further compressed into a TriSI descriptor using the principal component analysis technique. We tested our TriSI descriptor on the Bologna Dataset and compared it to several existing methods. Experimental results show that TriSI outperformed existing methods under all levels of noise and varying mesh resolutions. The TriSI was further tested to demonstrate its effectiveness in 3D modeling. Experimental results show that it can accurately perform pairwise and multiview range image registration. We finally used the TriSI descriptor for 3D object recognition. The results on the UWA Dataset show that TriSI outperformed the state-of-the-art methods including spin image, tensor and exponential map. The TriSI based method achieved a high recognition rate of 98.4%.