3D Modeling BIM Digital Twins Laser Scanning Lidar Research Surveying

Semantic Segmentation of Tunnel Point Cloud

graphic of tunnel semantic segmentation

Deep learning (DL) semantic segmentation of tunnel point cloud shows an efficient path for applications related to subway tunnel scenes, such as health inspection and building information modelling (BIM).

From a paper by Hgo Cui et al.

Current methods for tunnel point cloud segmentation often suffer from a shortage of benchmarks.

This paper proposes a large-scale, multi-modal dataset for semantic segmentation of subway tunnel point cloud called subway tunnel segmentation dataset (STSD). The STSD comprises point clouds and projected images annotated into 12 categories, encompassing three types of subway tunnels with a combined length exceeding 2700 m, totaling over 2.26 billion points.

A novel approach for DL semantic segmentation of subway tunnel point clouds is proposed herein. This approach enables the direct utilization of image-based DL segmentation networks on subway tunnel point clouds.

Furthermore, it incorporates a lossless coordinate transformation method capable of converting tunnel point clouds of any cross-section shape into images with minimal information loss. Further evaluation of several classic or state-of-the-art 2D and 3D DL semantic segmentation models shows the feasibility of the approach and dataset.

The best 2D model achieves a mIoU of 86.26% and outperforms the best 3D model by almost 10%. This research provides a novel approach for DL semantic segmentation in subway tunnel point clouds, contributes a large-scale, multi-modal dataset for the tunnel semantic segmentation, and creates a benchmark for further evaluation of the corresponding algorithms.

Graphical abstract for paper

For the complete paper CLICK HERE.

Note – If you liked this post click here to stay informed of all of the 3D laser scanning, geomatics, UAS, autonomous vehicle, Lidar News and more. If you have an informative 3D video that you would like us to promote, please forward to editor@lidarnews.com and if you would like to join the Younger Geospatial Professional movement click here

Leave a Comment

This site uses Akismet to reduce spam. Learn how your comment data is processed.