In this paper, a new approach for object detection and pose estimation is introduced. The contribution consists in the conception of entities permitting stable detection and reliable pose estimation of a given object. Thanks to a well-defined off-line learning phase, we design local and minimal subsets of feature points that have, at the same time, distinctive photometric and geometric properties. We call these entities Natural 3D Markers (N3Ms). Constraints on the selection and the distribution of the subsets coupled with a multi-level validation approach result in a detection at high frame rates and allow us to determine the precise pose of the object. The method is robust against noise, partial occlusions, background clutter and illumination changes. The experiments show its superiority to existing standard methods. The validation was carried out using simulated ground truth data. Excellent results on real data demonstrated the usefulness of this approach for many computer vision applications.     «

In this paper, a new approach for object detection and pose estimation is introduced. The contribution consists in the conception of entities permitting stable detection and reliable pose estimation of a given object. Thanks to a well-defined off-line learning phase, we design local and minimal subsets of feature points that have, at the same time, distinctive photometric and geometric properties. We call these entities Natural 3D Markers (N3Ms). Constraints on the selection and the distribution...     »