CAD-model recognition and 6DOF pose estimation using 3D cues
This paper focuses on developing a fast and accurate 3D feature for use in object recognition and pose estimation for rigid objects. More specifically, given a set of CAD models of different objects representing our knoweledge of the world - obtained using high-precission scanners that deliver accurate and noiseless data - our goal is to identify and estimate their pose in a real scene obtained by a depth sensor like the Microsoft Kinect. Borrowing ideas from the Viewpoint Feature Histogram (VFH) due to its computational efficiency and recognition performance, we describe the Clustered Viewpoint Feature Histogram (CVFH) and the cameras roll histogram together with our recognition framework to show that it can be effectively used to recognize objects and 6DOF pose in real environments dealing with partial occlusion, noise and different sensors atributes for training and recognition data. We show that CVFH out-performs VFH and present recognition results using the Microsoft Kinect Sensor on an object set of 44 objects.