To achieve fully autonomous navigation, vehicles need to compute an accurate model of their direct surrounding. In this paper, a 3D surface reconstruction algorithm from heterogeneous density 3D data is presented. The proposed method is based on a TSDF voxel-based representation, where an adaptive neighborhood kernel sourced on a Gaussian confidence evaluation is introduced. This enables to keep a good trade-off between the density of the reconstructed mesh and its accuracy. Experimental evaluations carried on both synthetic (CARLA) and real (KITTI) 3D data show a good performance compared to a state of the art method used for surface reconstruction.


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    Title :

    3D Surface Reconstruction from Voxel-based Lidar Data


    Contributors:


    Publication date :

    2019-10-01


    Size :

    3050986 byte




    Type of media :

    Conference paper


    Type of material :

    Electronic Resource


    Language :

    English



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