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RIU-Net: Embarrassingly simple semantic segmentation of 3D LiDAR point cloud

hal.structure.identifierLaboratoire Bordelais de Recherche en Informatique [LaBRI]
dc.contributor.authorBIASUTTI, Pierre
hal.structure.identifierLaboratoire Bordelais de Recherche en Informatique [LaBRI]
dc.contributor.authorBUGEAU, Aurélie
hal.structure.identifierInstitut de Mathématiques de Bordeaux [IMB]
dc.contributor.authorAUJOL, Jean-François
hal.structure.identifierMéthodes d'Analyses pour le Traitement d'Images et la Stéréorestitution [MATIS]
dc.contributor.authorBRÉDIF, Mathieu
dc.date.accessioned2024-04-04T03:01:03Z
dc.date.available2024-04-04T03:01:03Z
dc.identifier.urihttps://oskar-bordeaux.fr/handle/20.500.12278/192868
dc.description.abstractEnThis paper proposes RIU-Net (for Range-Image U-Net), the adaptation of a popular semantic segmentation network for the semantic segmentation of a 3D LiDAR point cloud. The point cloud is turned into a 2D range-image by exploiting the topology of the sensor. This image is then used as input to a U-net. This architecture has already proved its efficiency for the task of semantic segmentation of medical images. We propose to demonstrate how it can also be used for the accurate semantic segmentation of a 3D LiDAR point cloud. Our model is trained on range-images built from KITTI 3D object detection dataset. Experiments show that RIU-Net, despite being very simple, outperforms the state-of-the-art of range-image based methods. Finally, we demonstrate that this architecture is able to operate at 90fps on a single GPU, which enables deployment on low computational power systems such as robots.
dc.language.isoen
dc.title.enRIU-Net: Embarrassingly simple semantic segmentation of 3D LiDAR point cloud
dc.typeDocument de travail - Pré-publication
dc.subject.halInformatique [cs]/Vision par ordinateur et reconnaissance de formes [cs.CV]
dc.identifier.arxiv1905.08748
bordeaux.hal.laboratoriesInstitut de Mathématiques de Bordeaux (IMB) - UMR 5251*
bordeaux.institutionUniversité de Bordeaux
bordeaux.institutionBordeaux INP
bordeaux.institutionCNRS
hal.identifierhal-02136459
hal.version1
hal.origin.linkhttps://hal.archives-ouvertes.fr//hal-02136459v1
bordeaux.COinSctx_ver=Z39.88-2004&rft_val_fmt=info:ofi/fmt:kev:mtx:journal&rft.au=BIASUTTI,%20Pierre&BUGEAU,%20Aur%C3%A9lie&AUJOL,%20Jean-Fran%C3%A7ois&BR%C3%89DIF,%20Mathieu&rft.genre=preprint


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