The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences
Download
Publications Copernicus
Download
Citation
Articles | Volume XLVI-2/W1-2022
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLVI-2/W1-2022, 389–393, 2022
https://doi.org/10.5194/isprs-archives-XLVI-2-W1-2022-389-2022
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLVI-2/W1-2022, 389–393, 2022
https://doi.org/10.5194/isprs-archives-XLVI-2-W1-2022-389-2022

  25 Feb 2022

25 Feb 2022

AUTOMATIC POINT CLOUD NOISE MASKING IN CLOSE RANGE PHOTOGRAMMETRY FOR BUILDINGS USING AI-BASED SEMANTIC LABELLING

A. Murtiyoso1 and P. Grussenmeyer2 A. Murtiyoso and P. Grussenmeyer
  • 1Forest Resources Management Group, Institute of Terrestrial Ecosystems, Department of Environmental Systems Science, ETH Zürich, Switzerland
  • 2Université de Strasbourg, CNRS, INSA Strasbourg, ICube Laboratory UMR 7357, Photogrammetry and Geomatics Group, Strasbourg, France

Keywords: Photogrammetry, AI, Semantic segmentation, Image masks, Automation, Point cloud cleaning, 3D reconstruction

Abstract. The use of AI in semantic segmentation has grown significantly in recent years, aided by developments in computing power and the availability of annotated images for training data. However, in the context of close-range photogrammetry, although working with 2D images, AI is still used mostly for 3D point cloud segmentation purposes. In this paper, we propose a simple method to apply such methods in close range photogrammetry by benefitting from deep learning-based semantic segmentation. Specifically, AI was used to detect unwanted objects in a scene involving the 3D reconstruction of a historical building façade. For these purposes, classes e.g., sky, trees, and electricity poles were considered as noise. Masks were then created from the results which would then constraint the dense image matching process to only the wanted classes. In this regard, the resulting dense point cloud essentially projected the 2D semantic labels into the 3D space, thus excluding noise and unwanted object classes from the 3D scene. Our results were compared to manual image masking and managed to achieve comparable results while requiring only a fraction of the processing time when using a pre-trained DL network to do the task.