论文标题
结合本地和全球姿势估计以精确跟踪相似对象
Combining Local and Global Pose Estimation for Precise Tracking of Similar Objects
论文作者
论文摘要
在本文中，我们为潜在的相似和非纹理的对象提供了多对象6D检测和跟踪管道。用于对象分类的卷积神经网络与局部姿势改进和自动不匹配检测的组合可以在实时AR方案中进行直接应用。一个仅使用合成图像训练的新网络体系结构，允许同时构成多个对象，其中GPU存储器消耗减少和增强性能。此外，通过局部基于边缘的改进步骤，可以显式利用已知对象几何信息，从而进一步改善姿势估计。对于连续运动，局部改进的唯一使用会减少由于几何歧义或遮挡而引起的姿势不匹配。我们展示了整个跟踪管道，并展示了合并方法的好处。与基线方法相比，对一组具有挑战性的非纹理类似物体的实验证明了质量的增强。最后，我们说明了如何在建筑领域内的实际AR辅助应用中使用该系统。
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.
