论文标题
强大的基于RGB的6-DOF姿势估计，无实际姿势注释
Robust RGB-based 6-DoF Pose Estimation without Real Pose Annotations
论文作者
论文摘要
虽然从单个RGB图像中的6-DOF对象姿势估算中已经取得了很大进展，但当前的领先方法在很大程度上依赖于实批准数据。因此，它们仍然对严重的闭塞敏感，因为用带注释的数据覆盖所有可能的闭塞是棘手的。在本文中，我们介绍了一种方法，以稳健，准确地估算在有挑战性的条件下且无需使用任何真实姿势注释的6-DOF姿势。为此，我们利用网络从图像及其对应物合成为模拟闭塞的构图预测的直觉应该是一致的，并将其转化为自我监督的损失函数。我们对linemod，casluded-linemod，YCB和新的随机linemod数据集的实验证明了我们方法的鲁棒性。我们在linemod上实现了最先进的性能，并在没有实体设置的情况下，甚至在闭塞linemod上训练期间依靠真实注释的方法都超过了ockludedlinemod。
While much progress has been made in 6-DoF object pose estimation from a single RGB image, the current leading approaches heavily rely on real-annotation data. As such, they remain sensitive to severe occlusions, because covering all possible occlusions with annotated data is intractable. In this paper, we introduce an approach to robustly and accurately estimate the 6-DoF pose in challenging conditions and without using any real pose annotations. To this end, we leverage the intuition that the poses predicted by a network from an image and from its counterpart synthetically altered to mimic occlusion should be consistent, and translate this to a self-supervised loss function. Our experiments on LINEMOD, Occluded-LINEMOD, YCB and new Randomization LINEMOD dataset evidence the robustness of our approach. We achieve state of the art performance on LINEMOD, and OccludedLINEMOD in without real-pose setting, even outperforming methods that rely on real annotations during training on Occluded-LINEMOD.
