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Estimating the 6D pose and size of household containers is challenging due tolarge intra-class variations in the object properties, such as shape, size,appearance, and transparency. The task is made more difficult when theseobjects are held and manipulated by a person due to varying degrees of handocclusions caused by the type of grasps and by the viewpoint of the cameraobserving the person holding the object. In this paper, we present amixed-reality dataset of hand-occluded containers for category-level 6D objectpose and size estimation. The dataset consists of 138,240 images of renderedhands and forearms holding 48 synthetic objects, split into 3 grasp categoriesover 30 real backgrounds. We re-train and test an existing model for 6D objectpose estimation on our mixed-reality dataset. We discuss the impact of the useof this dataset in improving the task of 6D pose and size estimation.

A mixed-reality dataset for category-level 6D pose and size estimation of hand-occluded containers