ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences
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Articles | Volume X-4/W2-2022
ISPRS Ann. Photogramm. Remote Sens. Spatial Inf. Sci., X-4/W2-2022, 105–112, 2022
https://doi.org/10.5194/isprs-annals-X-4-W2-2022-105-2022
ISPRS Ann. Photogramm. Remote Sens. Spatial Inf. Sci., X-4/W2-2022, 105–112, 2022
https://doi.org/10.5194/isprs-annals-X-4-W2-2022-105-2022
 
14 Oct 2022
14 Oct 2022

SIMULATING LIDAR TO CREATE TRAINING DATA FOR MACHINE LEARNING ON 3D POINT CLOUDS

J. Hildebrand, S. Schulz, R. Richter, and J. Döllner J. Hildebrand et al.
  • Hasso Plattner Institute, University of Potsdam, Germany

Keywords: LiDAR, 3D Point Clouds, Data Synthesis, Machine Learning, Deep Learning, Semantic Segmentation

Abstract. 3D point clouds represent an essential category of geodata used in a variety of geoinformation applications. Typically, these applications require additional semantics to operate on subsets of the data like selected objects or surface categories. Machine learning approaches are increasingly used for classification. They operate directly on 3D point clouds and require large amounts of training data. An adequate amount of high-quality training data is often not available or has to be created manually. In this paper, we introduce a system for virtual laser scanning to create 3D point clouds with semantics information by utilizing 3D models. In particular, our system creates 3D point clouds with the same characteristics regarding density, occlusion, and scan pattern as those 3D point clouds captured in the real world. We evaluate our system with different data sets and show the potential to use the data to train neural networks for 3D point cloud classification.