论文标题
如何跟踪您的龙：实时RGB-D 6-DOF对象姿势跟踪的多重意义框架
How to track your dragon: A Multi-Attentional Framework for real-time RGB-D 6-DOF Object Pose Tracking
论文作者
论文摘要
我们提出了一种新型的多发卷积架构，以解决单个已知对象的实时RGB-D 6D对象姿势跟踪的问题。这样的问题构成了源于对象的性质及其与环境的互动的多种挑战，而以前的方法未能完全解决这些挑战。提出的框架封装了背景混乱和遮挡处理方法，通过将多个平行的软空间注意模块整合到多任务卷积神经网络（CNN）体系结构中。此外，我们考虑了对象的3D模型和姿势空间的特殊几何特性，并且在培训过程中使用了更复杂的方法进行数据增强。提供的实验结果证实了所提出的多重意义结构的有效性，因为它可以根据RGB-D对象跟踪的最新问题进行测试，从而将最新的旋转提高（SOA）跟踪性能提高了34.03％的平均得分为34.03％，旋转的旋转得分为40.01％。
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.
