The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences
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Articles | Volume XLIII-B3-2021
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLIII-B3-2021, 879–886, 2021
https://doi.org/10.5194/isprs-archives-XLIII-B3-2021-879-2021
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLIII-B3-2021, 879–886, 2021
https://doi.org/10.5194/isprs-archives-XLIII-B3-2021-879-2021

  29 Jun 2021

29 Jun 2021

3D URBAN CHANGE DETECTION WITH POINT CLOUD SIAMESE NETWORKS

I. de Gélis1,2, S. Lefèvre2, and T. Corpetti3 I. de Gélis et al.
  • 1Magellium, F-31000 Toulouse, France
  • 2Université Bretagne Sud, IRISA UMR 6074, F-56000 Vannes, France
  • 3CNRS, LETG UMR 6554, F-35000 Rennes, France

Keywords: 3D Change Detection, Point Clouds, Deep Learning, Siamese Network, Kernel Point Convolution, Urban Monitoring

Abstract. As the majority of the earth population is living in urban environments, cities are continuously evolving and efficient monitoring tools are needed to retrieve and classify their evolution. In this context, analysing changes between two dates is a crucial point. In urban environments, most changes occur along the vertical axis (with new construction or demolition of buildings) and the use of 3D data is therefore mandatory. Among them, LiDAR constitutes a valuable source of information. However, With the difficulty of processing sparse and unordered 3D point clouds, most of existing methods start by rasterizing point clouds (for example to Digital Surface Models) before using more conventional image processing tools. This implies a significant loss of information. Among existing studies dealing directly with point clouds, and to the best of our knowledge, no deep neural network-based method has been explored yet. Thus, in order to fill this gap and to test the ability of deep methods to deal with change detection and characterization of 3D point clouds, we propose a Siamese network with Kernel Point Convolution inspired by Siamese architectures that have already shown their performances on change detection in 2D images and on KPConv network which achieves high-quality results for semantic segmentation of raw 3D point clouds. We show quantitatively and qualitatively that our method outperforms by more than 25% (in terms of average Intersection over Union for classes of change) existing machine learning methods based on hand-crafted features.