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3D urban scene understanding by analysis of LiDAR, color and hyperspectral data

Abstract : Point clouds have attracted the interest of the research community over the last years. Initially, they were mostly used for remote sensing applications. More recently, thanks to the development of low-cost sensors and the publication of some opensource libraries, they have become very popular and have been applied to a wider range of applications. One of them is the autonomous vehicle where many efforts have been made in the last century to make it real. A very important bottleneck nowadays for the autonomous vehicle is the evaluation of the proposed algorithms. Due to the huge number of possible scenarios, it is not feasible to perform it in real life. An alternative is to simulate virtual environments where all possible configurations can be set up beforehand. However, they are not as realistic as the real world is. In this thesis, we studied the pertinence of including hyperspectral images in the creation of new virtual environments. Furthermore, we proposed new methods to improve 3D scene understanding for autonomous vehicles.
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Submitted on : Thursday, November 18, 2021 - 10:43:12 AM
Last modification on : Saturday, November 20, 2021 - 3:05:46 AM
Long-term archiving on: : Saturday, February 19, 2022 - 6:37:10 PM


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  • HAL Id : tel-03434199, version 1


David Duque-Arias. 3D urban scene understanding by analysis of LiDAR, color and hyperspectral data. Signal and Image Processing. Université Paris sciences et lettres, 2021. English. ⟨NNT : 2021UPSLM028⟩. ⟨tel-03434199⟩



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