论文标题
姿势提案评论家：通过学习再投影错误的良好姿势完善
Pose Proposal Critic: Robust Pose Refinement by Learning Reprojection Errors
论文作者
论文摘要
近年来，从单个RGB图像中估算刚性对象姿势的任务已经取得了长足的进步，但是对部分闭塞的稳健性仍然是一个具有挑战性的问题。通过渲染的姿势完善已显示出希望，以便在数据稀缺时尤其是改进的结果。 在本文中，我们将注意力集中在姿势改进上，并展示如何在部分闭塞的情况下进一步推动最先进的方法。提出的姿势改进方法利用了简化的学习任务，在该任务中，训练了CNN以估计观察到的图像和渲染图像之间的再投影误差。我们通过对纯合成数据以及合成和真实数据的混合物进行培训进行实验。在遮挡链圈基准测试中，三个指标中的两个指标中的两个指标的当前最新结果在最终度量标准中的表现优于三个度量。
In recent years, considerable progress has been made for the task of rigid object pose estimation from a single RGB-image, but achieving robustness to partial occlusions remains a challenging problem. Pose refinement via rendering has shown promise in order to achieve improved results, in particular, when data is scarce. In this paper we focus our attention on pose refinement, and show how to push the state-of-the-art further in the case of partial occlusions. The proposed pose refinement method leverages on a simplified learning task, where a CNN is trained to estimate the reprojection error between an observed and a rendered image. We experiment by training on purely synthetic data as well as a mixture of synthetic and real data. Current state-of-the-art results are outperformed for two out of three metrics on the Occlusion LINEMOD benchmark, while performing on-par for the final metric.
