LiRaFusion: Deep Adaptive LiDAR-Radar Fusion for 3D Object Detection
Fuente:
arXiv
Gespeichert in:
| Hauptverfasser: | , , |
|---|---|
| Format: | Preprint |
| Veröffentlicht: |
2024
|
| Schlagworte: | |
| Online-Zugang: | |
| Tags: |
Tag hinzufügen
Keine Tags, Fügen Sie den ersten Tag hinzu!
|
| _version_ | 1866911779395731456 |
|---|---|
| author | Song, Jingyu Zhao, Lingjun Skinner, Katherine A. |
| author_facet | Song, Jingyu Zhao, Lingjun Skinner, Katherine A. |
| contents | We propose LiRaFusion to tackle LiDAR-radar fusion for 3D object detection to fill the performance gap of existing LiDAR-radar detectors. To improve the feature extraction capabilities from these two modalities, we design an early fusion module for joint voxel feature encoding, and a middle fusion module to adaptively fuse feature maps via a gated network. We perform extensive evaluation on nuScenes to demonstrate that LiRaFusion leverages the complementary information of LiDAR and radar effectively and achieves notable improvement over existing methods. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2402_11735 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | LiRaFusion: Deep Adaptive LiDAR-Radar Fusion for 3D Object Detection Song, Jingyu Zhao, Lingjun Skinner, Katherine A. Robotics Computer Vision and Pattern Recognition We propose LiRaFusion to tackle LiDAR-radar fusion for 3D object detection to fill the performance gap of existing LiDAR-radar detectors. To improve the feature extraction capabilities from these two modalities, we design an early fusion module for joint voxel feature encoding, and a middle fusion module to adaptively fuse feature maps via a gated network. We perform extensive evaluation on nuScenes to demonstrate that LiRaFusion leverages the complementary information of LiDAR and radar effectively and achieves notable improvement over existing methods. |
| title | LiRaFusion: Deep Adaptive LiDAR-Radar Fusion for 3D Object Detection |
| topic | Robotics Computer Vision and Pattern Recognition |
| url | https://arxiv.org/abs/2402.11735 |