Publication: Sparse LiDAR and Stereo Fusion (SLS-Fusion) for Depth Estimation and 3D Object Detection
dc.affiliation.dpto | UC3M. Departamento de Informática | es |
dc.affiliation.grupoinv | UC3M. Grupo de Investigación: Inteligencia Artificial Aplicada (GIAA) | es |
dc.contributor.author | Mai, Nguyen-Anh-Minh | |
dc.contributor.author | Duthon, Pierre | |
dc.contributor.author | Khoudour, Louahdi | |
dc.contributor.author | Crouzil, Alain | |
dc.contributor.author | Velastin Carroza, Sergio Alejandro | |
dc.date.accessioned | 2021-06-02T10:27:20Z | |
dc.date.available | 2021-06-02T10:27:20Z | |
dc.date.issued | 2021-03-17 | |
dc.description | Procedings in: 11th International Conference on Pattern Recognition Systems (ICPRS-21), conference paper, 17-19 mar, 2021, Universidad de Talca, Curicó, Chile. | es |
dc.description.abstract | The ability to accurately detect and localize objects is recognized as being the most important for the perception of self-driving cars. From 2D to 3D object detection, the most difficult is to determine the distance from the ego-vehicle to objects. Expensive technology like LiDAR can provide a precise and accurate depth information, so most studies have tended to focus on this sensor showing a performance gap between LiDAR-based methods and camera-based methods. Although many authors have investigated how to fuse LiDAR with RGB cameras, as far as we know there are no studies to fuse LiDAR and stereo in a deep neural network for the 3D object detection task. This paper presents SLS-Fusion, a new approach to fuse data from 4-beam LiDAR and a stereo camera via a neural network for depth estimation to achieve better dense depth maps and thereby improves 3D object detection performance. Since 4-beam LiDAR is cheaper than the well-known 64-beam LiDAR, this approach is also classified as a low-cost sensors-based method. Through evaluation on the KITTI benchmark, it is shown that the proposed method significantly improves depth estimation performance compared to a baseline method. Also when applying it to 3D object detection, a new state of the art on low-cost sensor based method is achieved. | es |
dc.identifier.bibliographicCitation | Mai, N-A-M., et al. (2021, march). Sparse LiDAR and Stereo Fusion (SLS-Fusion) for Depth Estimation and 3D Object Detection. In: 11th International Conference on Pattern Recognition Systems (ICPRS-21), conference paper, 17-19 mar, 2021, Universidad de Talca, Curicó, Chile. | en |
dc.identifier.other | http://www.icprs.org/ | |
dc.identifier.publicationtitle | Sparse LiDAR and Stereo Fusion (SLS-Fusion) for Depth Estimation and 3D Object Detection | en |
dc.identifier.uri | https://hdl.handle.net/10016/32824 | |
dc.identifier.uxxi | CC/0000032474 | |
dc.language.iso | eng | |
dc.publisher | IET Digital Library | |
dc.relation.eventdate | 2021-03-17 | |
dc.relation.eventplace | Universidad de Talca, Curicó, Chile (conferencia virtual) | es |
dc.relation.eventtitle | 11th International Conference on Pattern Recognition Systems (ICPRS-21) | en |
dc.rights | © Institution of Engineering and Technology, 2021. | en |
dc.rights.accessRights | open access | |
dc.subject.eciencia | Informática | es |
dc.subject.other | Autonomous vehicle | en |
dc.subject.other | 3D object detection | en |
dc.subject.other | Depth completion | en |
dc.subject.other | Lidar stereo fusion | en |
dc.subject.other | Pseudo lidar | en |
dc.title | Sparse LiDAR and Stereo Fusion (SLS-Fusion) for Depth Estimation and 3D Object Detection | en |
dc.type | conference proceedings | * |
dc.type.hasVersion | AM | * |
dspace.entity.type | Publication |
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