Navigability Graph Extraction From Large-Scale 3D Point Cloud - Archive ouverte HAL
Communication Dans Un Congrès Année : 2018

Navigability Graph Extraction From Large-Scale 3D Point Cloud

Résumé

This paper presents a novel method for summarizing and compression of large-scale 3D models into compact spherical representations. The information is combined into a set of optimized spheres in order to facilitate its use by systems with limited resources (smartphones, robots, UAVs, ...). This vision-based summarizing process is applied in a fully automatic way using jointly photometric, geometric and semantic information of the studied environment. The main contribution of this research is to provide a navigability graph that maximizes the significance of the contents of its nodes while maintaining the full visibility of the environment. Experimental results in summarizing large-scale 3D map demonstrate the feasibility of our approach and evaluate the performance of the algorithm.
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Dates et versions

hal-01929921 , version 1 (21-11-2018)

Identifiants

Citer

Imeen Ben Salah, Sebastien Kramm, Cédric Demonceaux, Pascal Vasseur. Navigability Graph Extraction From Large-Scale 3D Point Cloud. 21st IEEE International Conference on Intelligent Transportation Systems, Nov 2018, Maui, Hawaii, United States. ⟨10.1109/ITSC.2018.8569447⟩. ⟨hal-01929921⟩
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