Semantically Guided Multi-View Stereo for Dense 3D Road Mapping

Compared to widely used LiDAR-based mapping in autonomous driving field, image-based mapping method has the advantages of low cost, high resolution, and no need for complex calibration. However, the image-based 3D mapping depends heavily on the texture richness and always leaves holes and outliers in low-textured areas, such as the road surface. To this end, this paper proposed a novel semantically guided Multi-View Stereo method for dense 3D road mapping, which integrates semantic information into PatchMatch-based MVS pipeline and uses image semantic segmentation as soft constraints in neighbor views selection, depth-map initialization, depth propagation, and depth-map completion. Experimental results on public and our own datasets show that, with the help of semantics, the proposed method achieves superior completeness with comparable accuracy for 3D road mapping compared to state-of-the-art MVS methods.

Automated summary

The paper proposes a novel semantically guided Multi-View Stereo method for dense 3D road mapping, which integrates semantic information into the process to improve completeness and handle holes and outliers in low-textured areas. Experimental results demonstrate that the method achieves superior completeness with comparable accuracy for 3D road mapping compared to state-of-the-art MVS methods.