CAD-based 3D objects recognition in monocular images for mobile augmented reality

• We analyze limitations of conventional recognition methods for mobile devices.
• Geometric features and internal sensors are used to reduce the search space.
• We determine objects azimuths by CAD models rendering and contours matching.
• All we need is only the object׳s CAD model without any other offline work.
• Our solution provides a novel perspective on recognition.

In order to improve registration performance of mobile augmented reality initialization, a new CAD-based recognition method of 3D objects in monocular images is proposed in this paper. Instead of estimating camera pose directly, our method attempts to estimate 3D object pose in the camera coordinate system. The geometric correspondence between the object mass center and its projection on 2D camera image is used to estimate object position. To hypothesize possible azimuths of the object, the 3D CAD model is off-screen rendered with OpenGL ES at different azimuths under an assumed inclination constraint provided by the inertial sensors of mobile devices, and compared with input image by contours matching. Unlike most recognition methods based on machine learning and natural features detection, which require large databases of real images and heavy computational consumption, our method only needs the CAD model of the 3D object in offline stage, and is computationally suitable for mobile devices. This paper describes the details of the method and shows its effectiveness with experiments.

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