Scalable and Detail-Preserving Ground Surface Reconstruction from Large 3D Point Clouds Acquired by Mobile Mapping Systems

Abstract : The currently existing mobile mapping systems equipped with active 3D sensors allow to acquire the environment with high sampling rates at high vehicle velocities. While providing an effective solution for environment sensing over large scale distances, such acquisi-tion provides only a discrete representation of the geometry. Thus, a continuous map of the underlying surface must be built. Mobile acquisition introduces several constraints for the state-of-the-art surface reconstruction algorithms. Smoothing becomes a difficult task for recovering sharp depth features while avoiding mesh shrinkage. In addition, interpolation-based techniques are not suitable for noisy datasets acquired by Mobile Laser Scanning (MLS) systems. Furthermore, scalability is a major concern for enabling real-time rendering over large scale distances while preserving geometric details. This paper presents a fully automatic ground surface recon-struction framework capable to deal with the aforementioned constraints. The proposed method exploits the quasi-flat geometry of the ground throughout a morphological segmentation algorithm. Then, a planar Delaunay triangulation is applied in order to reconstruct the ground surface. A smoothing procedure eliminates high frequency peaks, while preserving geometric details in order to provide a regular ground surface. Finally, a decimation step is applied in order to cope with scalability constraints over large scale distances. Experimental results on real data acquired in large urban environments are presented and a performance evaluation with respect to ground truth measurements demonstrate the effectiveness of our method.
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Daniela Craciun, Andrés Serna-Morales, Jean-Emmanuel Deschaud, Beatriz Marcotegui, François Goulette. Scalable and Detail-Preserving Ground Surface Reconstruction from Large 3D Point Clouds Acquired by Mobile Mapping Systems. PCV (Photogrammetric Computer Vision), Sep 2014, Zurich, Switzerland. pp.73 - 80, ⟨10.5194/isprsarchives-XL-3-73-2014⟩. ⟨hal-01097422⟩

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