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LU-Net, An efficient network for 3D LiDAR point cloud semantic segmentation based on end-to-end-learned 3D features and U-Net / Pierre Biasutti (2019)  

Public Titre : LU-Net, An efficient network for 3D LiDAR point cloud semantic segmentation based on end-to-end-learned 3D features and U-Net Type de document : Article/Communication Auteurs : Pierre Biasutti , Auteur ; Vincent Lepetit, Auteur ; Mathieu Brédif , Auteur ; Jean-François Aujol, Auteur ; Aurélie Bugeau, Auteur Editeur : New York : Institute of Electrical and Electronics Engineers IEEE Année de publication : 2019 Projets : 1-Pas de projet / Conférence : ICCVW 2019, IEEE/CVF International Conference on Computer Vision Workshop 27/10/2019 28/10/2019 Seoul Corée du sud Proceedings Importance : pp 942 - 950 Format : 21 x 30 cm Note générale : Bibliographie préprint dans HAL https://hal.archives-ouvertes.fr/hal-02269915v1 avec titre un peu différent - version finale dans HAL https://hal.archives-ouvertes.fr/hal-02269915v2 This project has also received funding from the European Union’s Horizon 2020 research and innovation programme under the Marie Skłodowska-Curie grant agreement No 777826. Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image [Termes IGN] données lidar [Termes IGN] données localisées 3D [Termes IGN] extraction de traits caractéristiques [Termes IGN] segmentation sémantique [Termes IGN] semis de points Résumé : (Auteur) We propose LU-Net (for LiDAR U-Net), for the semantic segmentation of a 3D LiDAR point cloud. Instead of applying some global 3D segmentation method such as Point-Net, we propose an end-to-end architecture for LiDAR point cloud semantic segmentation that efficiently solves the problem as an image processing problem. First, a high-level 3D feature extraction module is used to compute 3D local features for each point given its neighbors. Then, these features are projected into a 2D multichannel range-image by considering the topology of the sensor. This range-image later serves as the input to a U-Net segmentation network, which is a simple architecture yet enough for our purpose. In this way, we can exploit both the 3D nature of the data and the specificity of the LiDAR sensor. This approach efficiently bridges between 3D point cloud processing and image processing as it outperforms the state-of-the-art by a large margin on the KITTI dataset, as our experiments show. Moreover, this approach operates at 24fps on a single GPU. This is above the acquisition rate of common LiDAR sensors which makes it suitable for real-time applications. Numéro de notice : C2019-037 Affiliation des auteurs : LASTIG MATIS+Ext (2012-2019) Autre URL associée : vers HAL Thématique : IMAGERIE Nature : Communication nature-HAL : ComAvecCL&ActesPubliésIntl DOI : 10.1109/ICCVW.2019.00123 Date de publication en ligne : 05/03/2020 En ligne : https://doi.org/10.1109/ICCVW.2019.00123 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=93282