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Visibility Estimation and Joint Inpainting of Lidar Depth Maps

Abstract : This paper presents a novel variational image inpainting method to solve the problem of generating, from 3-D lidar measures, a dense depth map coherent with a given color image, tackling visibility issues. When projecting the lidar point cloud onto the image plane, we generally obtain a sparse depth map, due to undersampling. Moreover , lidar and image sensor positions generally differ during acquisition , such that depth values referring to objects that are hidden from the image view point might appear with a naive projection. The proposed algorithm estimates the complete depth map, while simultaneously detecting and excluding those hidden points. It consists in a primal-dual optimization method, where a coupled total variation regularization term is included to match the depth and image gradients and a visibility indicator handles the selection of visible points. Tests with real data prove the effectiveness of the proposed strategy.
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Submitted on : Tuesday, May 17, 2016 - 3:45:00 PM
Last modification on : Saturday, June 25, 2022 - 10:36:35 AM
Long-term archiving on: : Friday, August 19, 2016 - 4:54:16 PM


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  • HAL Id : hal-01316719, version 1




Marco Bevilacqua, Jean-François Aujol, Mathieu Brédif, Aurélie Bugeau. Visibility Estimation and Joint Inpainting of Lidar Depth Maps. IEEE International Conference on Image Processing (ICIP), Sep 2016, Phoenix, AZ, United States. ⟨hal-01316719⟩



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