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 for 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 40.5% for translation and 57.5% for rotation, when testing on the dataset presented in [1], the most complete dataset designed, up to date, for the problem of RGB-D object tracking.

提出一种新颖的多关注卷积架构，通过整合多个并行的软空间关注模块到多任务CNN架构中，来解决实时RGB-D 6D对象姿态跟踪问题，并考虑对象3D模型和姿态空间的特殊几何属性，使用更复杂的数据增强方法进行训练，实验证明该架构有效性得到了提高。

如何追踪你的龙：用于实时RGB-D 6-DOF物体姿态跟踪的多重关注框架