基于模型的增强现实无标识三维注册追踪方法

To tackle the huge searching space and few texture feature points on the object surface problem, we propose a novel method that combines model-based tracking and natural feature tracking for fast and robust camera pose calculation. The proposed method first adapts the state-of-the-art template matching method LINE-MOD into a scale invariant descriptor to recognize the target object and obtain the key-frame that is similar to the current viewpoint. Therefore, it greatly reduces the search space for pose refinement method and improves the searching speed. After that, the keyframe obtained from template matching stage is exploited to refine the tracking accuracy. In order to avoid tracking jitter and improve tracking accuracy and speed, RPnP algorithm is incorporated into our system. The experimental results and performance evaluations demonstrate that our method is fast, accurate and robust for 3D registration and tracking even under partial occlusion, and the tracking speed can reach 30 frame/s.