se（3）- TrackNet：通过校准合成域中的图像弹性来进行数据驱动的6D位姿跟踪
se(3)-TrackNet: Data-driven 6D Pose Tracking by Calibrating Image Residuals in Synthetic Domains
摘要：
跟踪视频序列中的物体的6D姿态对于机器人操作是重要的。然而，该任务引入了多个挑战：（i）机器人操纵涉及显著的遮挡;（ii）数据和注释是麻烦的并且难以针对6D姿态收集，这使机器学习解决方案复杂化，以及（iii）增量误差漂移通常在长期跟踪中累积，从而需要重新初始化对象的姿态。这项工作提出了一种数据驱动的优化方法，用于长期的6D姿态跟踪。它的目的是确定最佳的相对姿态给定的当前RGB-D观察和合成图像的条件下，以前的最佳估计和对象的模型。在这种情况下的关键贡献是一种新的神经网络架构，它适当地解开特征编码，以帮助减少域移位，并通过李代数有效的3D方向表示。因此，即使仅用合成数据训练网络，也可以在真实图像上有效地工作。在基准上的综合实验-现有的以及具有与对象操作相关的显著遮挡的新数据集-表明，所提出的方法实现了一致的鲁棒估计，并且优于替代方案，即使它们是用真实图像训练的。该方法也是计算效率最高的替代方案，并实现了90.9Hz的跟踪频率。
英文摘要：
Tracking the 6D pose of objects in video sequences is important for robot manipulation. This task, however, introduces multiple challenges: (i) robot manipulation involves significant occlusions; (ii) data and annotations are troublesome and difficult to collect for 6D poses, which complicates machine learning solutions, and (iii) incremental error drift often accumulates in long term tracking to necessitate re-initialization of the object's pose. This work proposes a data-driven optimization approach for long-term, 6D pose tracking. It aims to identify the optimal relative pose given the current RGB-D observation and a synthetic image conditioned on the previous best estimate and the object's model. The key contribution in this context is a novel neural network architecture, which appropriately disentangles the feature encoding to help reduce domain shift, and an effective 3D orientation representation via Lie Algebra. Consequently, even when the network is trained only with synthetic data can work effectively over real images. Comprehensive experiments over benchmarks - existing ones as well as a new dataset with significant occlusions related to object manipulation - show that the proposed approach achieves consistently robust estimates and outperforms alternatives, even though they have been trained with real images. The approach is also the most computationally efficient among the alternatives and achieves a tracking frequency of 90.9Hz.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
