Summarizing Large Scale 3D Mesh

Recent progress in 3D sensor devices and in semantic mapping allows to build very rich HD 3D maps very useful for autonomous navigation and localization. However, these maps are particularly huge and require important memory capabilities as well computational resources. In this paper, we propose a new method for summarizing a 3D map (Mesh)as a set of compact 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 very compact map that maximizes the significance of its content 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.