Real-Time Object Tracking in Sparse Point Clouds based on 3D Interpolation

While object tracking for 3D point clouds has been widely researched in recent years, most trackers employ a direct point-to-point matching method under the assumption that target object clouds are dense, although the method is not suitable for sparse point clouds. In this paper, we introduce a novel object-tracking strategy that enables even sparse point clouds to be tracked properly. The strategy involves estimating distributions, called as Estimation of Vertical Distributions (EVD), by the proposed interpolation method to augment data and by a point-to-distribution matching technique. The EVD step generates vertical distributions of unoccupied areas on a target object using the distributions of the occupied areas and then seeks the optimal solution through a coarse-to-fine grid search to guarantee real-time performance. In order to verify the proposed tracking algorithm, we have tested our tracker on real world data collected by our own platform, and the results have demonstrated that the tracker outperforms other trackers.