Abstract

This paper proposes a semantic segmentation pipeline for terrestrial laser scanning data. We achieve this by combining co-registered RGB and 3D point cloud information. Semantic segmentation is performed by applying a pre-trained off-the-shelf 2D convolutional neural network over a set of projected images extracted from a panoramic photograph. This allows the network to exploit the visual image features that are learnt in a state-of-the-art segmentation models trained on very large datasets. The study focuses on the adoption of the spherical information from the laser capture and assessing the results using image classification metrics. The obtained results demonstrate that the approach is a promising alternative for asset identification in laser scanning data. We demonstrate comparable performance with spherical machine learning frameworks, however, avoid both the labelling and training efforts required with such approaches.

Cite

@article{sanchez2021semantic,
  title={Semantic Segmentation of Terrestrial LIDAR Data Using Co-Registered RGB Data},
  author={Sanchez Castillo, E and Griffiths, D and Boehm, J},
  journal={The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences},
  volume={43},
  pages={223--229},
  year={2021},
  publisher={Copernicus GmbH}
}