LU-Net: An Efficient Network for 3D LiDAR Point Cloud Semantic Segmentation Based on End-to-End-Learned 3D Features and U-Net
| hal.structure.identifier | Laboratoire Bordelais de Recherche en Informatique [LaBRI] | |
| dc.contributor.author | BIASUTTI, Pierre | |
| hal.structure.identifier | Laboratoire Bordelais de Recherche en Informatique [LaBRI] | |
| dc.contributor.author | LEPETIT, Vincent | |
| hal.structure.identifier | Laboratoire des Sciences et Technologies de l'Information Géographique [LaSTIG] | |
| dc.contributor.author | BRÉDIF, Mathieu | |
| hal.structure.identifier | Institut de Mathématiques de Bordeaux [IMB] | |
| dc.contributor.author | AUJOL, Jean-François | |
| hal.structure.identifier | Laboratoire Bordelais de Recherche en Informatique [LaBRI] | |
| dc.contributor.author | BUGEAU, Aurélie | |
| dc.date.accessioned | 2024-04-04T02:59:25Z | |
| dc.date.available | 2024-04-04T02:59:25Z | |
| dc.date.conference | 2019-10-27 | |
| dc.identifier.uri | https://oskar-bordeaux.fr/handle/20.500.12278/192741 | |
| dc.description.abstractEn | We propose LU-Net---for LiDAR U-Net, a new method for the semantic segmentation of a 3D LiDAR point cloud. Instead of applying some global 3D segmentation method such as PointNet, we propose an end-to-end architecture for LiDAR point cloud semantic segmentation that efficiently solves the problem as an image processing problem. We first extract high-level 3D features for each point given its 3D neighbors. Then, these features are projected into a 2D multichannel range-image by considering the topology of the sensor. Thanks to these learned features and this projection, we can finally perform the segmentation using a simple U-Net segmentation network, which performs very well while being very efficient. In this way, we can exploit both the 3D nature of the data and the specificity of the LiDAR sensor. This approach 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. | |
| dc.language.iso | en | |
| dc.subject.en | point cloud | |
| dc.subject.en | cnn | |
| dc.subject.en | 3d | |
| dc.subject.en | lidar | |
| dc.subject.en | semantic segmentation | |
| dc.subject.en | u-net | |
| dc.title.en | LU-Net: An Efficient Network for 3D LiDAR Point Cloud Semantic Segmentation Based on End-to-End-Learned 3D Features and U-Net | |
| dc.type | Communication dans un congrès | |
| dc.identifier.doi | 10.1109/ICCVW.2019.00123 | |
| dc.subject.hal | Informatique [cs]/Vision par ordinateur et reconnaissance de formes [cs.CV] | |
| bordeaux.hal.laboratories | Institut de Mathématiques de Bordeaux (IMB) - UMR 5251 | * |
| bordeaux.institution | Université de Bordeaux | |
| bordeaux.institution | Bordeaux INP | |
| bordeaux.institution | CNRS | |
| bordeaux.conference.title | IEEE International Conference on Computer Vision Workshops (ICCV) | |
| bordeaux.country | KR | |
| bordeaux.conference.city | Séoul | |
| bordeaux.peerReviewed | oui | |
| hal.identifier | hal-02269915 | |
| hal.version | 1 | |
| hal.invited | non | |
| hal.proceedings | oui | |
| hal.popular | non | |
| hal.audience | Internationale | |
| hal.origin.link | https://hal.archives-ouvertes.fr//hal-02269915v1 | |
| bordeaux.COinS | ctx_ver=Z39.88-2004&rft_val_fmt=info:ofi/fmt:kev:mtx:journal&rft.au=BIASUTTI,%20Pierre&LEPETIT,%20Vincent&BR%C3%89DIF,%20Mathieu&AUJOL,%20Jean-Fran%C3%A7ois&BUGEAU,%20Aur%C3%A9lie&rft.genre=unknown |
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