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3D Surface Reconstruction from Voxel-based Lidar Data

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Abstract

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.

Dates and versions

hal-02165672 , version 1 (26-06-2019)

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Luis Roldão, Raoul de Charette, Anne Verroust-Blondet. 3D Surface Reconstruction from Voxel-based Lidar Data. ITSC 2019 - IEEE Intelligent Transportation Systems Conference, Oct 2019, Auckland, New Zealand. ⟨hal-02165672⟩

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