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| Main Authors: | , , , , , , , |
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| Format: | Preprint |
| Published: |
2024
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2404.16304 |
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| _version_ | 1866910422944186368 |
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| author | Dong, Zhiwei Zhu, Xi Cao, Xiya Ding, Ran Li, Wei Zhou, Caifa Wang, Yongliang Liu, Qiangbo |
| author_facet | Dong, Zhiwei Zhu, Xi Cao, Xiya Ding, Ran Li, Wei Zhou, Caifa Wang, Yongliang Liu, Qiangbo |
| contents | Lane detection has made significant progress in recent years, but there is not a unified architecture for its two sub-tasks: 2D lane detection and 3D lane detection. To fill this gap, we introduce BézierFormer, a unified 2D and 3D lane detection architecture based on Bézier curve lane representation. BézierFormer formulate queries as Bézier control points and incorporate a novel Bézier curve attention mechanism. This attention mechanism enables comprehensive and accurate feature extraction for slender lane curves via sampling and fusing multiple reference points on each curve. In addition, we propose a novel Chamfer IoU-based loss which is more suitable for the Bézier control points regression. The state-of-the-art performance of BézierFormer on widely-used 2D and 3D lane detection benchmarks verifies its effectiveness and suggests the worthiness of further exploration. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2404_16304 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | BezierFormer: A Unified Architecture for 2D and 3D Lane Detection Dong, Zhiwei Zhu, Xi Cao, Xiya Ding, Ran Li, Wei Zhou, Caifa Wang, Yongliang Liu, Qiangbo Computer Vision and Pattern Recognition Lane detection has made significant progress in recent years, but there is not a unified architecture for its two sub-tasks: 2D lane detection and 3D lane detection. To fill this gap, we introduce BézierFormer, a unified 2D and 3D lane detection architecture based on Bézier curve lane representation. BézierFormer formulate queries as Bézier control points and incorporate a novel Bézier curve attention mechanism. This attention mechanism enables comprehensive and accurate feature extraction for slender lane curves via sampling and fusing multiple reference points on each curve. In addition, we propose a novel Chamfer IoU-based loss which is more suitable for the Bézier control points regression. The state-of-the-art performance of BézierFormer on widely-used 2D and 3D lane detection benchmarks verifies its effectiveness and suggests the worthiness of further exploration. |
| title | BezierFormer: A Unified Architecture for 2D and 3D Lane Detection |
| topic | Computer Vision and Pattern Recognition |
| url | https://arxiv.org/abs/2404.16304 |