论文标题
基于边缘增强点对的3D刚性对象的6DOF姿势估计
6DOF Pose Estimation of a 3D Rigid Object based on Edge-enhanced Point Pair Features
论文作者
论文摘要
点对特征（PPF）广泛用于6D姿势估计。在本文中，我们提出了一种基于PPF框架的有效的6D姿势估计方法。我们引入了一个靶向良好的下采样策略，该策略更多地集中在边缘区域，以有效地提取复杂的几何形状。提出了一种姿势假设验证方法来通过计算边缘匹配度来解决对称歧义。我们对两个具有挑战性的数据集和一个现实世界中收集的数据集进行评估，证明了我们方法对几何复杂，遮挡，对称对象的姿势估计的优越性。我们通过将其应用于模拟穿刺来进一步验证我们的方法。
The point pair feature (PPF) is widely used for 6D pose estimation. In this paper, we propose an efficient 6D pose estimation method based on the PPF framework. We introduce a well-targeted down-sampling strategy that focuses more on edge area for efficient feature extraction of complex geometry. A pose hypothesis validation approach is proposed to resolve the symmetric ambiguity by calculating edge matching degree. We perform evaluations on two challenging datasets and one real-world collected dataset, demonstrating the superiority of our method on pose estimation of geometrically complex, occluded, symmetrical objects. We further validate our method by applying it to simulated punctures.
