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吴继春, 杨永达, 张斋武, 张平, 范大鹏. 一种基于点云的实时的类别级位姿估计[J]. 计算机辅助设计与图形学学报.
引用本文: 吴继春, 杨永达, 张斋武, 张平, 范大鹏. 一种基于点云的实时的类别级位姿估计[J]. 计算机辅助设计与图形学学报.
Jichun Wu, Yongda Yang, Zhaiwu Zhang, Ping Zhang, . Centroid and pose estimation of occluded objects based on deep learning[J]. Journal of Computer-Aided Design & Computer Graphics.
Citation: Jichun Wu, Yongda Yang, Zhaiwu Zhang, Ping Zhang, . Centroid and pose estimation of occluded objects based on deep learning[J]. Journal of Computer-Aided Design & Computer Graphics.

一种基于点云的实时的类别级位姿估计

Centroid and pose estimation of occluded objects based on deep learning

  • 摘要: 针对传统的视觉算法的无法准确测量物体3D质心和尺寸、无法检测数据集中未出现过的物体等问题, 本文提出了一种基于点云的类别级物体质心与位姿估计的方法. 同时通过本文提到的方法进行数据集制作, 大大降低了数据集的制作成本. 从单张RGBD图像中恢复同一类别未见过的对象实例, 即数据集中没有的实例, 从而估计其质心和位姿. 通过二维算法先对物体类别进行确定并对检测区域进行分割, 结合深度图得到观测到的点云, 将RGB图像、观测到的点云以及先验形状输入网络中, 输出一个变形场以及对应矩阵. 将先验形状进行变形然后规范到NOCS坐标下, 通过Umeyama算法进行配准. 从而确定物体的类别和位姿, 并对物体质心进行计算, 同时使用BlendMask进行二维分割实现了实时的位姿估计. 仿真实验证明了该方法的准确性以及可靠性, 同时证明了通过点云的方法计算的质心要优于其他方法.

     

    Abstract: Aiming at the problems that traditional visual algorithms can't accurately measure the 3D centroid and size of objects and can't detect objects that haven't appeared in the data set, this paper proposes a point cloud-based centroid and pose estimation method for class-level objects.  At the same time, the method mentioned in this paper can greatly reduce the production cost of data set.  The unseen object instances of the same category are recovered from a single RGBD image, i.e. the instances that do not exist in the data set, so as to estimate their centroid and pose.  The object category is first determined by a two-dimensional algorithm and the detection area is divided. The observed point cloud is obtained by combining with the depth map. The RGB image, observed point cloud and prior shape are input into the network to output a deformation field and corresponding matrix.  The prior shapes were deformed and then normalized to NOCS coordinates, and registration was carried out by Umeyama algorithm.  Thus, the class and pose of the object are determined, and the centroid of the object is calculated. Meanwhile, BlendMask is used for two-dimensional segmentation to realize real-time pose estimation.  Simulation results show the accuracy and reliability of the proposed method, and the point cloud method is superior to other methods in calculating the center of mass.

     

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