Three-dimensional object tracking and pose estimation represent a pivotal area of research in computer vision, with applications ranging from augmented reality to autonomous navigation and robotics.

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Researchers have adapted deep learning techniques in a multi-object tracking framework, overcoming short-term occlusion and achieving remarkable performance without sacrificing computational speed.

Region-based methods have become increasingly popular for model-based, monocular 3D tracking of texture-less objects in cluttered scenes. However, while they achieve state-of-the-art results, most ...