论文标题
斑布：对6DOF对象姿势估计的粗到细表面编码
ZebraPose: Coarse to Fine Surface Encoding for 6DoF Object Pose Estimation
论文作者
论文摘要
建立从图像到3D的对应关系长期以来一直是6DOF对象姿势估计的关键任务。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。在遮挡存在下，密集方法还改善了姿势估计。最近，研究人员通过学习对象片段作为细分表明了改进。在这项工作中，我们提出一个离散的描述符，可以密集地表示对象表面。通过合并分层二进制组，我们可以非常有效地编码对象表面。此外，我们提出了一种粗糙至精细的训练策略，可以实现细粒的对应性预测。最后，通过将预测的代码与对象表面匹配并使用PNP求解器，我们估算了6DOF姿势。公共LM-O和YCB-V数据集的结果对W.R.T.的状态显示出重大改进。在某些情况下，添加（-s）度量，甚至超过基于RGB-D的方法。
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.
