In this paper, we present a multi-object 6D detection and tracking pipeline for potentially similar and non-textured objects. The combination of a convolutional neural network for object classification and rough pose estimation with a local pose refinement and an automatic mismatch detection enables direct application in real-time AR scenarios. A new network architecture, trained solely with synthetic images, allows simultaneous pose estimation of multiple objects with reduced GPU memory consumption and enhanced performance. In addition, the pose estimates are further improved by a local edge-based refinement step that explicitly exploits known object geometry information. For continuous movements, the sole use of local refinement reduces pose mismatches due to geometric ambiguities or occlusions. We showcase the entire tracking pipeline and demonstrate the benefits of the combined approach. Experiments on a challenging set of non-textured similar objects demonstrate the enhanced quality compared to the baseline method. Finally, we illustrate how the system can be used in a real AR assistance application within the field of construction.

翻译：在本文中,我们为潜在的相似和非透光天体提出了一个多点 6D 探测和跟踪管道。物体分类和粗度估计的进化神经网络与局部面貌的改进和自动错配探测相结合,使得能够直接应用于实时AR情景中。一个仅受过合成图像培训的新网络结构允许同时对多个天体进行估计,其GPU内存消耗减少,性能增强。此外,通过一个基于边缘的改进步骤,明确利用已知物体几何学信息,使表面估计得到进一步改进。对于连续移动而言,仅仅利用本地精细度的改进可以减少几何模糊或隐蔽性造成的不匹配。我们展示整个跟踪管道,并展示综合方法的好处。关于一组具有挑战性的非透光性类似天体的实验表明,与基线方法相比,质量有所提高。最后,我们说明该系统如何用于建筑领域的真正AR援助应用。