Accurate 3D lane detection from monocular images presents significant challenges due to depth ambiguity and imperfect ground modeling. Previous attempts to model the ground have often used a planar ground assumption with limited degrees of freedom, making them unsuitable for complex road environments with varying slopes. Our study introduces HeightLane, an innovative method that predicts a height map from monocular images by creating anchors based on a multi-slope assumption. This approach provides a detailed and accurate representation of the ground. HeightLane employs the predicted heightmap along with a deformable attention-based spatial feature transform framework to efficiently convert 2D image features into 3D bird's eye view (BEV) features, enhancing spatial understanding and lane structure recognition. Additionally, the heightmap is used for the positional encoding of BEV features, further improving their spatial accuracy. This explicit view transformation bridges the gap between front-view perceptions and spatially accurate BEV representations, significantly improving detection performance. To address the lack of the necessary ground truth (GT) height map in the original OpenLane dataset, we leverage the Waymo dataset and accumulate its LiDAR data to generate a height map for the drivable area of each scene. The GT heightmaps are used to train the heightmap extraction module from monocular images. Extensive experiments on the OpenLane validation set show that HeightLane achieves state-of-the-art performance in terms of F-score, highlighting its potential in real-world applications.

翻译：从单目图像中实现精确的三维车道线检测面临着深度模糊性和不完善地面建模的重大挑战。先前尝试对地面进行建模的方法通常采用自由度有限的平面地面假设，使其难以适用于具有变化坡度的复杂道路环境。本研究提出HeightLane，一种创新方法，通过基于多坡度假设创建锚点，从单目图像预测高度图。该方法提供了细致且准确的地面表征。HeightLane利用预测的高度图以及基于可变形注意力的空间特征变换框架，将二维图像特征高效转换为三维鸟瞰图（BEV）特征，从而增强空间理解与车道结构识别能力。此外，高度图被用于BEV特征的位置编码，进一步提升其空间准确性。这种显式的视图变换弥合了前视图感知与空间精确的BEV表征之间的差距，显著提升了检测性能。针对原始OpenLane数据集缺乏必要的地面真值（GT）高度图的问题，我们利用Waymo数据集并累积其LiDAR数据，为每个场景的可行驶区域生成高度图。这些GT高度图用于训练从单目图像提取高度图的模块。在OpenLane验证集上的大量实验表明，HeightLane在F分数指标上达到了最先进的性能，凸显了其在现实应用中的潜力。