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Sémantisation à la volée de nuages de points 3D acquis par systèmes embarqués

Abstract : This thesis is at the confluence of two worlds in rapid growth: autonomous cars and artificial intelligence (especially deep learning). As the first takes advantage of the second, autonomous vehicles are increasingly using deep learning methods to analyze the data produced by its various sensors (including LiDARs) and to make decisions. While deep learning methods have revolutionized image analysis (in classification and segmentation for example), they do not produce such spectacular results on 3D point clouds. This is particularly true because the datasets of annotated 3D point clouds are rare and of moderate quality. This thesis therefore presents a new dataset developed by mobile acquisition to produce enough data and annotated by hand to ensure a good quality of segmentation. In addition, these datasets are inherently unbalanced in number of samples per class and contain many redundant samples, so a sampling method adapted to these datasets is proposed. Another problem encountered when trying to classify a point from its neighbourhood as a voxel grid is the compromise between a fine discretization step (for accurately describing the surface adjacent to the point) and a large grid (to look for context a little further away). We therefore also propose network methods that take advantage of multi-scale neighbourhoods. These methods achieve the state of the art of point classification methods on public benchmarks. Finally, to respect the constraints imposed by embedded systems (real-time processing and low computing power), we present a method that allows convolutional layers to be applied only where there is information to be processed.
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Submitted on : Tuesday, February 16, 2021 - 9:00:14 AM
Last modification on : Thursday, February 18, 2021 - 3:11:49 AM


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  • HAL Id : tel-03142440, version 1


Xavier Roynard. Sémantisation à la volée de nuages de points 3D acquis par systèmes embarqués. Apprentissage [cs.LG]. Université Paris sciences et lettres, 2019. Français. ⟨NNT : 2019PSLEM078⟩. ⟨tel-03142440⟩



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