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| Autores principales: | , , , , , , |
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| Formato: | Preprint |
| Publicado: |
2024
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| Materias: | |
| Acceso en línea: | https://arxiv.org/abs/2405.15843 |
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| _version_ | 1866911886926151680 |
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| author | Foucard, Louis Khanna, Samar Shi, Yi Liu, Chi-Kuei Shen, Quinn Z Ngo, Thuyen Xia, Zi-Xiang |
| author_facet | Foucard, Louis Khanna, Samar Shi, Yi Liu, Chi-Kuei Shen, Quinn Z Ngo, Thuyen Xia, Zi-Xiang |
| contents | In this paper, we propose SpotNet: a fast, single stage, image-centric but LiDAR anchored approach for long range 3D object detection. We demonstrate that our approach to LiDAR/image sensor fusion, combined with the joint learning of 2D and 3D detection tasks, can lead to accurate 3D object detection with very sparse LiDAR support. Unlike more recent bird's-eye-view (BEV) sensor-fusion methods which scale with range $r$ as $O(r^2)$, SpotNet scales as $O(1)$ with range. We argue that such an architecture is ideally suited to leverage each sensor's strength, i.e. semantic understanding from images and accurate range finding from LiDAR data. Finally we show that anchoring detections on LiDAR points removes the need to regress distances, and so the architecture is able to transfer from 2MP to 8MP resolution images without re-training. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2405_15843 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | SpotNet: An Image Centric, Lidar Anchored Approach To Long Range Perception Foucard, Louis Khanna, Samar Shi, Yi Liu, Chi-Kuei Shen, Quinn Z Ngo, Thuyen Xia, Zi-Xiang Computer Vision and Pattern Recognition Artificial Intelligence In this paper, we propose SpotNet: a fast, single stage, image-centric but LiDAR anchored approach for long range 3D object detection. We demonstrate that our approach to LiDAR/image sensor fusion, combined with the joint learning of 2D and 3D detection tasks, can lead to accurate 3D object detection with very sparse LiDAR support. Unlike more recent bird's-eye-view (BEV) sensor-fusion methods which scale with range $r$ as $O(r^2)$, SpotNet scales as $O(1)$ with range. We argue that such an architecture is ideally suited to leverage each sensor's strength, i.e. semantic understanding from images and accurate range finding from LiDAR data. Finally we show that anchoring detections on LiDAR points removes the need to regress distances, and so the architecture is able to transfer from 2MP to 8MP resolution images without re-training. |
| title | SpotNet: An Image Centric, Lidar Anchored Approach To Long Range Perception |
| topic | Computer Vision and Pattern Recognition Artificial Intelligence |
| url | https://arxiv.org/abs/2405.15843 |