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We present a novel multi-attentional convolutional architecture to tackle theproblem of real-time RGB-D 6D object pose tracking of single, known objects.Such a problem poses multiple challenges originating both from the objects'nature and their interaction with their environment, which previous approacheshave failed to fully address. The proposed framework encapsulates methods forbackground clutter and occlusion handling by integrating multiple parallel softspatial attention modules into a multitask Convolutional Neural Network (CNN)architecture. Moreover, we consider the special geometrical properties of boththe object's 3D model and the pose space, and we use a more sophisticatedapproach for data augmentation during training. The provided experimentalresults confirm the effectiveness of the proposed multi-attentionalarchitecture, as it improves the State-of-the-Art (SoA) tracking performance byan average score of 34.03% for translation and 40.01% for rotation, when testedon the most complete dataset designed, up to date,for the problem of RGB-Dobject tracking.

How to track your dragon: A Multi-Attentional Framework for real-time RGB-D 6-DOF Object Pose Tracking