3D CNN based phantom object removing from mobile laser scanning data

In this paper we introduce a new deep learning based approach to detect and remove phantom objects from point clouds produced by mobile laser scanning (MLS) systems. The phantoms are caused by the presence of scene objects moving concurrently with the MLS platform, and appear as long, sparse but irregular point cloud segments in the measurements. We propose a new 3D CNN framework working on a voxelized column-grid to identify the phantom regions. We quantitatively evaluate the proposed model on real MLS test data, and compare it to two different reference approaches.