In this paper, we propose an advanced approach in targeting the problem of monocular 3D lane detection by leveraging geometry structure underneath the process of 2D to 3D lane reconstruction. Inspired by previous methods, we first analyze the geometry heuristic between the 3D lane and its 2D representation on the ground and propose to impose explicit supervision based on the structure prior, which makes it achievable to build inter-lane and intra-lane relationships to facilitate the reconstruction of 3D lanes from local to global. Second, to reduce the structure loss in 2D lane representation, we directly extract BEV lane information from front view images, which tremendously eases the confusion of distant lane features in previous methods. Furthermore, we propose a novel task-specific data augmentation method by synthesizing new training data for both segmentation and reconstruction tasks in our pipeline, to counter the imbalanced data distribution of camera pose and ground slope to improve generalization on unseen data. Our work marks the first attempt to employ the geometry prior information into DNN-based 3D lane detection and makes it achievable for detecting lanes in an extra-long distance, doubling the original detection range. The proposed method can be smoothly adopted by other frameworks without extra costs. Experimental results show that our work outperforms state-of-the-art approaches by 3.8% F-Score on Apollo 3D synthetic dataset at real-time speed of 82 FPS without introducing extra parameters.
翻译:在本文中,我们提出了一种先进的方法,通过在2D至3D车道重建过程中利用几何结构来利用二D至三D车道重建过程的几何结构来应对单眼3D车道探测问题。在以往方法的启发下,我们首先分析三D车道与地面2D车道代表之间的几何超度,并提议根据先前的结构实行明确的监督,从而可以建立线际和内部线内关系,以促进从地方到全球重建三D车道。第二,为了减少2D车道代表处的结构损失,我们直接从前视图像中提取BEV车道信息,这大大缓解了以往方法中遥远车道特征的混乱。此外,我们提出了一种新的特定任务数据增强方法,将我们管道的分段和重建任务的新培训数据加以综合,以对抗摄影机床和地面坡道之间不平衡的数据分配,从而改进对隐蔽数据的一般化。我们的工作标志着第一次尝试在DNNE3D车道探测中利用先前的几何测量信息,并使它能够在较远的距离探测行道,使最初的航道参数比重三D的航道特性参数上翻倍。我们最初的探测程三FA型三的探程表上的拟议方法可以顺利地展示F-38的FF-38式方法,在不采用不采用其他方法,在不使用其他的探程上采用其他探程上采用其他方法。