Guo Jianwei, Xing Xuejun, Quan Weize, Yan Dong-Ming, Gu Qingyi, Liu Yang, Zhang Xiaopeng
IEEE Trans Image Process. 2021;30:5072-5084. doi: 10.1109/TIP.2021.3078109. Epub 2021 May 19.
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.
我们提出了一种新颖且高效的方法，用于估计由点云表示的复杂场景中已知物体的6D物体姿态。我们的方法基于著名的点对特征（PPF）匹配，该方法利用自相似点对来计算潜在匹配，从而通过投票方案为物体姿态投票。本文的主要贡献在于提出了一种改进的基于PPF的识别框架，特别是一种基于物体中心与点对特征之间相对几何关系的新的中心投票策略。利用这种几何关系，我们首先向物体中心生成投票，在真实物体中心附近形成投票簇。然后我们对这些投票进行分组和汇总，以生成一组姿态假设。最后，执行姿态验证算子以滤除误报并预测目标物体的合适6D姿态。我们的方法也适用于解决多实例和多物体检测任务。在各种具有挑战性的基准数据集上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、传感器噪声和几何形状简单的物体具有判别力且鲁棒。通过与现有最先进方法进行比较，进一步验证了我们工作的优势。
