论文标题
类别级别6D姿势的混合现实数据集和手工封闭式容器的尺寸估计
A mixed-reality dataset for category-level 6D pose and size estimation of hand-occluded containers
论文作者
论文摘要
由于对象特性（例如形状，尺寸，外观和透明度）的较大类内变化，估计家庭容器的6D姿势和大小是具有挑战性的。当这些物体被人的手工闭塞造成的不同程度的手动阻塞以及观察持有物体的人观察到的相机的观点而导致的手动遮挡造成的不同程度的手动阻塞，使该任务变得更加困难。在本文中，我们提出了一个用于类别级别6D对象姿势和尺寸估计的手工封闭容器的混合现实数据集。该数据集由138,240张渲染的手和前臂的图像组成，持有48个合成对象，分为30个真实背景的3个掌握类别。我们在混合现实数据集中重新培训并测试现有模型的6D对象姿势估计。我们讨论了该数据集使用在改善6D姿势和尺寸估计任务方面的影响。
Estimating the 6D pose and size of household containers is challenging due to large intra-class variations in the object properties, such as shape, size, appearance, and transparency. The task is made more difficult when these objects are held and manipulated by a person due to varying degrees of hand occlusions caused by the type of grasps and by the viewpoint of the camera observing the person holding the object. In this paper, we present a mixed-reality dataset of hand-occluded containers for category-level 6D object pose and size estimation. The dataset consists of 138,240 images of rendered hands and forearms holding 48 synthetic objects, split into 3 grasp categories over 30 real backgrounds. We re-train and test an existing model for 6D object pose estimation on our mixed-reality dataset. We discuss the impact of the use of this dataset in improving the task of 6D pose and size estimation.
