Multi-scale superquadric fitting for efficient shape and pose recovery of unknown objects

Rapidly acquiring the shape and pose information of unknown objects is an essential characteristic of modern robotic systems in order to perform efficient manipulation tasks. In this work, we present a framework for 3D geometric shape recovery and pose estimation from unorganized point cloud data. We propose a low latency multi-scale voxelization strategy that rapidly fits superquadrics to single view 3D point clouds. As a result, we are able to quickly and accurately estimate the shape and pose parameters of relevant objects in a scene. We evaluate our approach on two datasets of common household objects collected using Microsoft's Kinect sensor. We also compare our work to the state of the art and achieve comparable results in less computational time. Our experimental results demonstrate the efficacy of our approach.