大论文11.Covariance based point cloud descriptors for object detection and recognition.pdfVIP

Computer Vision and Image Understanding 142 (2016) 80–93 Contents lists available at ScienceDirect Computer Vision and Image Understanding journal homepage: /locate/cviu Covariance based point cloud descriptors for object detection and recognition✩ Duc Fehr, William J. Beksi, Dimitris Zermas, Nikolaos Papanikolopoulos∗ Department of Computer Science and Engineering, University of Minnesota, Minneapolis, MN, 55455 USA a r t i c l e i n f o a b s t r a c t Article history: Processing 3D point cloud data is of primary interest in many areas of computer vision, including object Received 22 March 2015 grasping, robot navigation, and object recognition. The introduction of affordable RGB-D sensors has created Accepted 17 June 2015 a great interest in the computer vision community towards developing efficient algorithms for point cloud Available online 26 June 2015 processing. Previously, capturing a point cloud required expensive specialized sensors such as lasers or ded- Keywords: icated range imaging devices; now, range data is readily available from low-cost sensors that provide easily RGB-D data extractable point clouds from a depth map. From here, an interesting challenge is to find different objects in Colored point clouds the point cloud. Various descriptors have been introduced to match features in a point cloud. Cheap sensors Classification are not ne