Driving simulation engines represent a cost effective solution for vehicle development, being employed for performing feasibility studies and tests failure and for assessing new functionalities. Nevertheless, they require geometrically accurate and realistic three-dimensional (3D) models in order to allow driver training. This paper presents the Automatic Ground Surface Reconstruction method, a framework that exploits 3D data acquired by Mobile Laser Scanning systems. They are particularly attractive due to their fast acquisition at the terrestrial level. Nevertheless, such a mobile acquisition introduces several constraints for the existing 3D surface reconstruction algorithms. The proposed surface modeling framework produces a regular surface and recovers sharp depth features within a scalable and detail-preserving framework. Experimental results on real data acquired in urban environments allow us to conclude on the effectiveness of the proposed method.
BouchnerPNovotnyS. Car dynamics model - Design for interactive driving simulation use. In: Proceedings of the 2nd international conference on Applied informatics and computing theory, Prague, Czech Republic, 26–28 September 2011, pp. 285–289.
4.
PetitJ.Génération, visualization et èvaluation d’images (HDR). Application la simulation de conduite nocturne. PhD Dissertation, Université Claude Bernard Lyon I, France2010.
5.
BrémondR.La visibilite routiere: une approche pluri-disciplinaire. HdR Université Paris-Est, 2010.
6.
ChapronTColinotJP. The new PSA Peugeot-Citroen advanced driving simulator: overall design and motion cue algorithm. In: Proceedings of Driving Simulator Conference North America, Iowa City, USA, 12–14 September 2007. Iowa City, IA: National Advanced Driving Simulator, University of Iowa.
7.
BollingAJanssonJGenellA. Shake: an approach for realistic simulation of rough roads in a moving base driving simulator. In: Proceedings of the driving simulation conference Europe 2010 (ed KemenyAMérienneFEspiéS), Paris, France, 6–7 September 2012, pp.135–143. INRETS.
CarlbergMAndrewsJGaoP. Fast surface reconstruction and segmentation with ground-based and airborne lidar range data. In: 4th Symposium on 3D Data Processing, Visualization, and Transmission (3DPVT), Atlanta, USA, 18–20 June 2008, pp.97–104.
12.
WiemannTNuchterALingemannK. Automatic construction of polygonal maps from point cloud data. In: 2010 IEEE Safety Security and Rescue Robotics, Bremen, Germany, 26–30 July 2010, pp.1–6. IEEE.
13.
YuSJSukumarSRKoschanAF. 3D reconstruction of road surfaces using an integrated multi-sensory approach. Opt Laser Eng2007; 45: 808–818.
14.
JaakkolaAHyyppaJHyyppaH. Retrieval algorithms for road surface modelling using laser-based mobile mapping. Sensors2008; 8: 5238–5249.
15.
AlexaMBehrJCohen-orD. Computing and rendering point set surfaces. IEEE Trans Comput Vis Graph2003; 9: 3–15.
16.
OtzireliACGuennebaudGGrossM.Feature preserving point set surfaces based on non-linear kernel regression. Eurographics2009; 28: 493–501.
17.
EdelsbrunnerH. Shape reconstruction with Delaunay complex. In: LATIN’98 Theoretical Informatics: Third Latin American Symposium (ed LucchesiCLMouraAV), Campinas, Brazil, 20–24 April 1998, pp. 119–132. Berlin: Springer Berlin Heidelberg.
18.
GopiMKrishnanSSilvaCT.Surface reconstruction based on lower dimensional localized Delaunay triangulation. Comput Graph Forum2000; 19: 467–478.
19.
KazhdanMBolithoMHoppeH.Poisson surface reconstruction. In: Proceedings of the fourth eurographics symposium on geometry processing, Cagliari, Italy, 26–28 June 2006, pp. 61–70. Switzerland: Eurographics Association.
20.
MartonZCRusuRBBeetzM. On fast surface reconstruction methods for large and noisy point clouds. In: 2009 IEEE International Conference on Robotics and Automation, Kobe, Japan, 12–17 May 2009, pp. 3218–3223. IEEE.
21.
BohrenJFooteTKellerJ. Litte Ben: The Ben Franklin racing team’s entry in the 2007 DARPA urban challenge. J Field Robot2008; 25: 598–614.
22.
SernaAMarcoteguiB.Detection, segmentation and classification of 3D urban objects using mathematical morphology and supervised learning. ISPRS J Photogramm Remote Sens2014; 93: 243–255.
23.
MatheronG.Random sets and integral geometry. Vol.28. New York: John Wiley & Sons, 1975, pp.493–501.
ShewchukJR. Triangle: Engineering a 2D quality mesh generator and Delaunay triangulator. In: LinMCManochaD (eds) Applied computational geometry: towards geometric engineering. Vol. 1148. Berlin: Springer, 1996, pp.203–222.
26.
TaubinGZhangTGolubG.Optimal surface smoothing as filter design. IBM Res Rep1996; 1148: 203–222.
27.
SchroederWJZargeJALorensenWE. Decimation of triangle meshes. In: Proceedings of the 19th annual conference on Computer graphics and interactive techniques, Chicago, USA, 27–31 July 1992, pp. 65–70. New York: ACM. Conference proceedings of SIGGRAPH, 1992.
28.
HoppeH. Progressive meshes. In: Proceedings of the 23rd annual conference on Computer graphics and interactive techniques, New Orleans, USA, 4–9 August 1996, pp. 99–108. New York: ACM.
29.
PaparoditisNPapelardJCannelleB. Stereopolis II: a multi-purpose and multi-sensor 3D mobile mapping system for street visualization and 3D metrology. Revue francaise de photogrammètrie et de tèlèdètection, 2012; 200: 69–79.
30.
GouletteFNashashibiFAbuhadrousI. An integrated on-board laser range sensing system for on the way city and road modelling. Int Arch Photogrammetr Remote Sens Spatial Inform Sci2006; 34 (Part A): 3–5.
31.
SchroederWMartinK. The Visualization Toolkit (eds HansenCDJohnsonCR). Elsevier, 2004.
32.
TurnetEZakhorA. Watertight planar surface meshing of indoor points clouds with voxel carving. In: Proceedings of the 2013 International Conference on 3D Vision, Seattle, USA, 29 June–01 July 2013, pp.41–48. Washington DC: IEEE Computer Society.
33.
CignoniPRocchiniCScopignoR.Measuring error on simplified surfaces. Comput Graph Forum1998; 17: 167–174.
34.
RusuRBCousinsS.3D is here: Point Cloud Library (PCL). In: 2011 IEEE International Conference on Robotics and Automation (ICRA), Shanghai, China, 9–13 May 2011, pp.1–4.
