3D object pose detection using foreground/background segmentation

This paper addresses the challenge of detecting and localizing a poorly textured known object, by initially estimating its complete 3D pose in a video sequence. Our solution relies on the 3D model of the object and synthetic views. The full pose estimation process is then based on foreground/background segmentation and on an efficient probabilistic edge-based matching and alignment procedure with the set of synthetic views, classified through an unsupervised learning phase. Our study focuses on space robotics applications and the method has been tested on both synthetic and real images, showing its efficiency and convenience, with reasonable computational costs.