ZebraPose：用于6自由度物体位姿估计的由粗到细表面编码
ZebraPose: Coarse to Fine Surface Encoding for 6DoF Object Pose Estimation
摘要：
建立从图像到3D的对应关系一直是6DoF目标位姿估计的关键任务。为了更准确地预测姿势，深入学习的密集地图取代了稀疏模板。密集的方法也改善了姿态估计存在的闭塞。最近，研究人员通过将对象片段学习为分割来显示改进。在这项工作中，我们提出了一个离散的描述符，它可以表示物体表面密集。通过结合分层二进制分组，我们可以非常有效地编码对象表面。此外，我们提出了一种由粗到细的训练策略，可以实现细粒度的对应预测。最后，通过匹配预测代码与物体表面，并使用一个PADOF求解器，我们估计的6DoF位姿。公共LM-O和YCB-V数据集上的结果显示，相对于最先进的w.r.t. ADD（-S）度量，在某些情况下甚至超过了基于RGB-D的方法。
英文摘要：
Establishing correspondences from image to 3D has been a key task of 6DoF object pose estimation for a long time. To predict pose more accurately, deeply learned dense maps replaced sparse templates. Dense methods also improved pose estimation in the presence of occlusion. More recently researchers have shown improvements by learning object fragments as segmentation. In this work, we present a discrete descriptor, which can represent the object surface densely. By incorporating a hierarchical binary grouping, we can encode the object surface very efficiently. Moreover, we propose a coarse to fine training strategy, which enables fine-grained correspondence prediction. Finally, by matching predicted codes with object surface and using a PnP solver, we estimate the 6DoF pose. Results on the public LM-O and YCB-V datasets show major improvement over the state of the art w.r.t. ADD(-S) metric, even surpassing RGB-D based methods in some cases.
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。
