Abstract
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.
Abstract (translated)
近年来，在车道检测方面取得了显著的进步，但二维和三维车道检测的两个子任务并没有一个统一的架构。为了填补这一空白，我们引入了BézierFormer，一种基于Bézier曲线车道表示的统一二维和三维车道检测架构。BézierFormer将查询表示为Bézier控制点，并引入了一种新颖的Bézier曲线注意力机制。这种注意力机制通过采样和融合每个曲线上的多个参考点，实现对细小车道曲线的全面而准确的特征提取。此外，我们提出了一种新的Chamfer IoU基于损失，该损失更适合Bézier控制点回归。BézierFormer在广泛使用的二维和三维车道检测基准测试中的最先进性能证实了其有效性和进一步探索的必要性。
URL
https://arxiv.org/abs/2404.16304
