This paper presents a method for estimating the six Degrees of Freedom (6DoF) pose of texture-less primitive-shaped objects from depth images. As the conventional methods for object pose estimation require rich texture or geometric features to the target objects, these methods are not suitable for texture-less and geometrically simple shaped objects. In order to estimate the pose of the primitive-shaped object, the parameters that represent primitive shapes are estimated. However, these methods explicitly limit the number of types of primitive shapes that can be estimated. We employ superquadrics as a primitive shape representation that can represent various types of primitive shapes with only a few parameters. In order to estimate the superquadric parameters of primitive-shaped objects, the point cloud of the object must be segmented from a depth image. It is known that the parameter estimation is sensitive to outliers, which are caused by the miss-segmentation of the depth image. Therefore, we propose a novel estimation method for superquadric parameters that are robust to outliers. In the experiment, we constructed a dataset in which the person grasps and moves the primitive-shaped objects. The experimental results show that our estimation method outperformed three conventional methods and the baseline method.

中文翻译：

---

使用超二次表示从深度图像中估计原始形状对象的姿势

本文提出了一种从深度图像估计无纹理原始形状对象的六个自由度（6DoF）姿势的方法。由于用于对象姿态估计的常规方法要求目标对象具有丰富的纹理或几何特征，因此这些方法不适用于无纹理且几何形状简单的形状的对象。为了估计原始形状对象的姿势，估计代表原始形状的参数。但是，这些方法明确限制了可以估计的基本形状的类型数量。我们采用超二次元作为原始形状表示形式，仅用几个参数就可以表示各种类型的原始形状。为了估算原始形状对象的超二次参数，必须从深度图像中分割出对象的点云。已知参数估计对异常值敏感，这是由于深度图像的误分割引起的。因此，我们提出了一种对异常值具有鲁棒性的超二次参数估计方法。在实验中，我们构建了一个数据集，人们可以在其中抓取并移动原始形状的对象。实验结果表明，我们的估计方法优于三种常规方法和基准方法。我们构建了一个数据集，人们可以在其中抓取并移动原始形状的对象。实验结果表明，我们的估计方法优于三种常规方法和基准方法。我们构建了一个数据集，人们可以在其中抓取并移动原始形状的对象。实验结果表明，我们的估计方法优于三种常规方法和基准方法。