  The Large-scale 3D reconstruction, texturing and semantic mapping are
nowadays widely used for automated driving vehicles, virtual reality and
automatic data generation. However, most approaches are developed for RGB-D
cameras with colored dense point clouds and not suitable for large-scale
outdoor environments using sparse LiDAR point clouds. Since a 3D surface can be
usually observed from multiple camera images with different view poses, an
optimal image patch selection for the texturing and an optimal semantic class
estimation for the semantic mapping are still challenging.
  To address these problems, we propose a novel 3D reconstruction, texturing
and semantic mapping system using LiDAR and camera sensors. An Adaptive
Truncated Signed Distance Function is introduced to describe surfaces
implicitly, which can deal with different LiDAR point sparsities and improve
model quality. The from this implicit function extracted triangle mesh map is
then textured from a series of registered camera images by applying an optimal
image patch selection strategy. Besides that, a Markov Random Field-based data
fusion approach is proposed to estimate the optimal semantic class for each
triangle mesh. Our approach is evaluated on a synthetic dataset, the KITTI
dataset and a dataset recorded with our experimental vehicle. The results show
that the 3D models generated using our approach are more accurate in comparison
to using other state-of-the-art approaches. The texturing and semantic mapping
achieve also very promising results.
