  We present a novel multi-attentional convolutional architecture to tackle the
problem 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 approaches
have failed to fully address. The proposed framework encapsulates methods for
background clutter and occlusion handling by integrating multiple parallel soft
spatial attention modules into a multitask Convolutional Neural Network (CNN)
architecture. Moreover, we consider the special geometrical properties of both
the object's 3D model and the pose space, and we use a more sophisticated
approach for data augmentation during training. The provided experimental
results confirm the effectiveness of the proposed multi-attentional
architecture, as it improves the State-of-the-Art (SoA) tracking performance by
an average score of 34.03% for translation and 40.01% for rotation, when tested
on the most complete dataset designed, up to date,for the problem of RGB-D
object tracking.
