Poisson Mesh Reconstruction for Accurate Object Tracking With Low-Fidelity Point Clouds

Abstract

Tracking refers to a set of techniques that allows one to calculate the position and orientation of an object with respect to a global reference coordinate system in real time. A common method for tracking with point clouds is the iterative closest point (ICP) algorithm, which relies on the continuous matching of sequential sampled point clouds with a reference point cloud. Modern commodity range cameras provide point cloud data that can be used for that purpose. However, this point cloud data is generally considered as low-fidelity and insufficient for accurate object tracking. Mesh reconstruction algorithms can improve the fidelity of the point cloud by reconstructing the overall shape of the object. This paper explores the potential for point cloud fidelity improvement via the Poisson mesh reconstruction (PMR) algorithm and compares the accuracy with a common ICP-based tracking technique and a local mesh reconstruction operator. The results of an offline simulation are promising.