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.
@article{arxiv.1906.10515,
title = {3D Surface Reconstruction from Voxel-based Lidar Data},
author = {Luis Roldão and Raoul de Charette and Anne Verroust-Blondet},
journal= {arXiv preprint arXiv:1906.10515},
year = {2019}
}
Comments
IEEE Intelligent Transportation Systems Conference (ITSC) 2019