论文标题
重新审视3D对象姿势估计的模板：对新对象的概括和稳健性对闭塞
Templates for 3D Object Pose Estimation Revisited: Generalization to New Objects and Robustness to Occlusions
论文作者
论文摘要
我们提出了一种可以识别新对象并估算其在RGB图像中的3D姿势的方法，即使在部分遮挡下也是如此。我们的方法不需要这些对象上的训练阶段，也不需要描绘它们的真实图像，只需要它们的CAD模型。它依靠一小部分训练对象来学习本地对象表示，这使我们可以在本地匹配输入图像与一组“模板”，这是新对象的CAD模型的图像。与最先进的方法相反，应用我们的方法的新对象可能与训练对象大不相同。结果，我们是第一个显示概括而不在linemod和coctlusion-linemod数据集上进行概括的人。我们对以前基于模板方法的故障模式的分析进一步证实了局部特征对模板匹配的好处。我们的表现要优于linemod，coclusion-linemod和t-less数据集上的最新模板匹配方法。我们的源代码和数据可在https://github.com/nv-nguyen/template-pose上公开获取。
We present a method that can recognize new objects and estimate their 3D pose in RGB images even under partial occlusions. Our method requires neither a training phase on these objects nor real images depicting them, only their CAD models. It relies on a small set of training objects to learn local object representations, which allow us to locally match the input image to a set of "templates", rendered images of the CAD models for the new objects. In contrast with the state-of-the-art methods, the new objects on which our method is applied can be very different from the training objects. As a result, we are the first to show generalization without retraining on the LINEMOD and Occlusion-LINEMOD datasets. Our analysis of the failure modes of previous template-based approaches further confirms the benefits of local features for template matching. We outperform the state-of-the-art template matching methods on the LINEMOD, Occlusion-LINEMOD and T-LESS datasets. Our source code and data are publicly available at https://github.com/nv-nguyen/template-pose
