3D CNN Based Phantom Object Removing from Mobile Laser Scanning Data

Nagy, Balázs and Benedek, Csaba (2017) 3D CNN Based Phantom Object Removing from Mobile Laser Scanning Data. In: International Joint Conference on Neural Networks, May 14–19, 2017, Anchorage, Alaska, USA.

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Abstract

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.

Item Type: Conference or Workshop Item (Lecture)
Subjects: Q Science > QA Mathematics and Computer Science > QA75 Electronic computers. Computer science / számítástechnika, számítógéptudomány
Divisions: Distributed Events Analysis Research Laboratory
Depositing User: Csaba Benedek
Date Deposited: 06 Feb 2017 08:41
Last Modified: 02 Dec 2017 13:19
URI: http://eprints.sztaki.hu/id/eprint/9065

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