Automatic Object Shape Completion from 3D Point Clouds for Object Manipulation

3D object representations should be able to model the shape at different levels, considering both low-level and high-level shape descriptions. In robotics applications, is difficult to compute the shape descriptors in self-occluded point clouds while solving manipulation tasks. In this paper we propose an object completion method that under some assumptions works well for a large set of kitchenware objects, based on Principal Component Analysis (PCA). In addition, object manipulation in robotics must consider not only the shape but the of actions that an agent may perform. Thus, shape-only descriptions are limited because do not consider where the object is located with respect to others and the type of constraints associated to manipulation actions. In this paper, we define a set of semantic parts (i.e. bounding boxes) that consider grasping constraints of kitchenware objects, and how to segment the object into those parts. The semantic parts provide a general representation across object categories, which allows to reduce the grasping hypotheses. Our algorithm is able to find the semantic parts of kitchenware objects in and efficient way.