3D object recognition in range images using visibility context

Recognizing and localizing queried objects in range images plays an important role for robotic manipulation and navigation. Even though it has been steadily studied, it is still a challenging task for scenes with occlusion and clutter. We present a novel approach to object recognition that boosts dissimilarity between queried objects and similar-shaped background objects in the scene by maximizing use of the visibility context. We design a new point pair feature containing discriminative description inferred from the visibility context. Also, we propose a pose estimation method that accurately localizes objects using these point pair matches. Finally, two measures of validity are suggested to discard false detections. With 10 query objects, our approach is evaluated on depth images of cluttered office scenes captured from a real-time range sensor. The experimental results demonstrate that our method remarkably outperforms two state-of-the-art methods in terms of recognition (recall & precision) and runtime performance.