论文标题
M $^2 $ -3DLANENET：探索多模式3D车道检测
M$^2$-3DLaneNet: Exploring Multi-Modal 3D Lane Detection
论文作者
论文摘要
由于其稀疏和纤细的性质，估算3D空间中准确的车道线仍然具有挑战性。先前的工作主要集中于使用图像进行3D车道检测，从而导致固有的投影误差和几何信息的丢失。为了解决这些问题，我们探讨了利用LiDar进行3D车道检测的潜力，无论是独立方法还是与现有的单眼方法结合使用。在本文中，我们提出了M $^2 $ -3DLANENET，以整合来自多个传感器的互补信息。具体而言，M $^2 $ -3DLANENENET通过通过深度完成将LIDAR数据中的几何信息提升到3D空间。随后，通过跨模式BEV融合，通过LIDAR特征进一步增强了提起的2D功能。大规模OpenLane数据集上的广泛实验证明了M $^2 $ -3DLANENEN的有效性，无论范围如何（75m或100m）。
Estimating accurate lane lines in 3D space remains challenging due to their sparse and slim nature. Previous works mainly focused on using images for 3D lane detection, leading to inherent projection error and loss of geometry information. To address these issues, we explore the potential of leveraging LiDAR for 3D lane detection, either as a standalone method or in combination with existing monocular approaches. In this paper, we propose M$^2$-3DLaneNet to integrate complementary information from multiple sensors. Specifically, M$^2$-3DLaneNet lifts 2D features into 3D space by incorporating geometry information from LiDAR data through depth completion. Subsequently, the lifted 2D features are further enhanced with LiDAR features through cross-modality BEV fusion. Extensive experiments on the large-scale OpenLane dataset demonstrate the effectiveness of M$^2$-3DLaneNet, regardless of the range (75m or 100m).
