We present a novel and efficient approach to estimate 6D object poses of known objects in complex scenes represented by point clouds. Our approach is based on the well-known point pair feature (PPF) matching, which utilizes self-similar point pairs to compute potential matches and thereby cast votes for the object pose by a voting scheme. The main contribution of this paper is to present an improved PPF-based recognition framework, especially a new center voting strategy based on the relative geometric relationship between the object center and point pair features. Using this geometric relationship, we first generate votes to object centers resulting in vote clusters near real object centers. Then we group and aggregate these votes to generate a set of pose hypotheses. Finally, a pose verification operator is performed to filter out false positives and predict appropriate 6D poses of the target object. Our approach is also suitable to solve the multi-instance and multi-object detection tasks. Extensive experiments on a variety of challenging benchmark datasets demonstrate that the proposed algorithm is discriminative and robust towards similar-looking distractors, sensor noise, and geometrically simple shapes. The advantage of our work is further verified by comparing to the state-of-the-art approaches.

中文翻译：

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用于 3D 点云中对象检测和 6D 姿态估计的高效中心投票


我们提出了一种新颖且有效的方法来估计由点云表示的复杂场景中已知物体的 6D 物体姿态。我们的方法基于众所周知的点对特征（PPF）匹配，它利用自相似点对来计算潜在的匹配，从而通过投票方案为对象姿势投票。本文的主要贡献是提出了一种改进的基于PPF的识别框架，特别是一种基于目标中心和点对特征之间的相对几何关系的新的中心投票策略。利用这种几何关系，我们首先生成对对象中心的投票，从而在真实对象中心附近产生投票簇。然后我们对这些投票进行分组和汇总，以生成一组姿势假设。最后，执行姿势验证算子以过滤掉误报并预测目标对象的适当 6D 姿势。我们的方法也适用于解决多实例和多对象检测任务。对各种具有挑战性的基准数据集进行的大量实验表明，所提出的算法对于外观相似的干扰物、传感器噪声和几何简单形状具有区分性和鲁棒性。通过与最先进的方法进行比较，进一步验证了我们工作的优势。