HSDF-Lane: 高度对齐符号距离场与语义车道先验用于3D车道检测
HSDF-Lane: Height-Aligned Signed Distance Field with Semantic Lane Prior for 3D Lane Detection
AI总结：
提出HSDF-Lane，通过高度对齐符号距离场隐式建模道路表面，结合可微渲染生成准确高度图和表面对齐特征，并引入车道感知语义位置编码注入车道先验，在OpenLane基准上实现3D车道检测和高度图估计的最优性能。
摘要：
单目3D车道检测在自动驾驶中扮演关键角色，但由于固有的深度模糊性，从单张图像恢复可靠的3D几何仍然具有挑战性。先前的方法在平坦地面假设下将图像特征投影到鸟瞰图（BEV）空间，导致真实道路上的几何畸变。近期方法转而预测显式高度图以捕捉非平面表面，但仍依赖稀疏的基于锚点的回归，并且仅将恢复的几何用于空间变换而非语义理解。为克服这些限制，我们提出HSDF-Lane，该方法在密集采样的3D特征体上隐式地将道路表面建模为高度对齐符号距离场（HSDF）。通过可微渲染，HSDF联合生成准确的高度图和表面对齐特征。我们进一步引入车道感知语义位置编码（LSPE），将从表面对齐特征中导出的车道存在先验注入到Transformer查询中，将几何结构与语义引导相结合。在OpenLane基准上的大量实验表明，HSDF-Lane在3D车道检测和高度图估计方面均达到了最先进的性能。
英文摘要：
Monocular 3D lane detection plays a critical role in autonomous driving, yet recovering reliable 3D geometry from a single image remains challenging due to inherent depth ambiguity. Prior methods project image features into Bird's-Eye-View (BEV) space under a flat-ground assumption, causing geometric distortion on real-world roads. Recent methods instead predict explicit height maps to capture non-planar surfaces, but still rely on sparse anchor-based regression and exploit the recovered geometry merely for spatial transformation rather than semantic understanding. To overcome these limitations, we propose HSDF-Lane, which implicitly models the road surface as a Height-aligned Signed Distance Field (HSDF) over a densely sampled 3D feature volume. Through differentiable rendering, the HSDF jointly produces an accurate height map and surface-aligned features. We further introduce Lane-aware Semantic Positional Encoding (LSPE), which injects a lane-existence prior derived from the surface-aligned features into the transformer queries, coupling geometric structure with semantic guidance. Extensive experiments on the OpenLane benchmark show that HSDF-Lane achieves state-of-the-art performance in both 3D lane detection and height map estimation.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
