A direct method for robust model-based 3D object tracking from a monocular RGB image

This paper proposes a novel method for robust 3D object tracking from a monocular RGB image when an object model is available. The proposed method is based on direct image alignment between consecutive frames over a 3D target object. Unlike conventional direct methods that only rely on image intensity, we newly model intensity variations using the surface normal of the object under the Lambertian assumption. From the prediction about image intensity in this model, we also employ a constrained objective function, which significantly alleviates degradation of the tracking performance. In experiments, we evaluate our method using datasets that consist of test sequences under challenging conditions, and demonstrate its benefits compared to other methods.