Object pose estimation is crucial to many industrial applications, with one example being automated spray painting using a robot. However, confidentiality concerns often limit access to high-quality 3D models, posing a significant challenge for point-cloud-based pose estimation. In such scenarios, rotational symmetry, a readily accessible characteristic of many industrial objects, can provide valuable prior information to facilitate pose estimation.In this paper, we propose a method that leverages the rotational symmetry commonly found in industrial objects to address the challenge caused by the absence of 3D models. The object pose is jointly estimated with point cloud refinement through an iterative optimization process. This optimization relies on a rotational symmetry constraint loss. To construct this loss, each 3D point is rotated according to the currently estimated pose, and multiple correspondences are identified using nearest-neighbor search by exploiting the rotational symmetry property. These correspondences are then used to compute the rotational symmetry constraint loss, which iteratively refines both the pose and the point cloud.By explicitly incorporating rotational symmetry into the optimization process, the proposed method achieves robust pose estimation and generalizes well across diverse object types. The proposed method is evaluated on a dataset specifically created for point clouds without known 3D models, consisting of four categories of synthetic objects and one real wheel hub collected from a production line. Experimental results demonstrate that the proposed method achieves performance comparable to methods that rely on known 3D models.

翻译：物体姿态估计对许多工业应用至关重要，例如使用机器人进行自动化喷涂。然而，保密性限制通常阻碍了高质量三维模型的获取，这对基于点云的姿态估计构成了重大挑战。在此类场景中，旋转对称性——许多工业物体易于获取的特征——可提供有价值的先验信息以促进姿态估计。本文提出一种利用工业物体普遍存在的旋转对称性来解决缺乏三维模型所导致挑战的方法。通过迭代优化过程，物体姿态与点云细化被联合估计，该优化基于旋转对称性约束损失函数。为构建此损失，每个三维点根据当前估计姿态进行旋转，并利用旋转对称性通过最近邻搜索确定多个对应关系。随后，这些对应关系被用于计算旋转对称性约束损失，迭代优化姿态与点云。通过将旋转对称性显式融入优化过程，所提方法实现了鲁棒的姿态估计，并在不同物体类型间具有良好的泛化性能。该方法在一个专门为无已知三维模型的点云创建的数据集上进行了评估，该数据集包含四类合成物体与一个从生产线采集的真实轮毂。实验结果表明，所提方法达到了与依赖已知三维模型的方法相当的性能。