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Detecting and Correcting Shadows in Urban Point Clouds and Image Collections

Abstract : LiDAR (Light Detection And Ranging) acquisition is a widespread method for measuring urban scenes, be it a small town neighborhood or an entire city. It is even more interesting when this acquisition is coupled with a collection of pictures registered with the data, permitting to recover the color information of the points. Yet, this added color can be perturbed by shadows that are very dependent on the sun direction and weather conditions during the acquisition. In this paper, we focus on the problem of automatically detecting and correcting the shadows from the LiDAR data by exploiting both the images and the point set laser reflectance. Building on the observation that shadow boundaries are characterized by both a significant color change and a stable laser reflectance, we propose to first detect shadow boundaries in the point set and then segment ground shadows using graph cuts in the image. Finally using a simplified illumination model we correct the shadows directly on the colored point sets. This joint exploitation of both the laser point set and the images renders our approach robust and efficient, avoiding user interaction.
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Submitted on : Tuesday, November 8, 2016 - 2:42:03 PM
Last modification on : Tuesday, June 1, 2021 - 2:08:08 PM
Long-term archiving on: : Tuesday, March 14, 2017 - 7:35:59 PM


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  • HAL Id : hal-01393998, version 1


Maximilien Guislain, Julie Digne, Raphaëlle Chaine, Dimitri Kudelski, Pascal Lefebvre-Albaret. Detecting and Correcting Shadows in Urban Point Clouds and Image Collections. 2016 International Conference on 3D Vision (3DV), Oct 2016, Stanford, United States, Oct 2016, Stanford, United States. 9p. ⟨hal-01393998⟩



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