Detection and Recognition of Compound 3D Models by Hypothesis Generation
In the paper there is proposed an integrated object detection and recognition system, based on object description given in semantic form . The objects are described in a generic way in terms of parts and relations between them. The Bayesian inference system is utilized, so each object detection and recognition score has probabilistic interpretation. There are designed basic 3D models founded on the inference framework. Object instances are then detected and recognized in real-world Kinect RGBD images.