用隐式距离场提升单目3D车道检测精度
HSDF-Lane: Height-Aligned Signed Distance Field with Semantic Lane Prior for 3D Lane Detection
- 构建高度对齐的符号距离场，显式建模非平面路面
- 在OpenLane上3D车道检测与高程图估计均达领先水平
- 融合车道语义先验，兼顾几何结构与语义理解
单目3D车道检测在自动驾驶中至关重要，但单张图像固有的深度模糊性使其恢复可靠3D几何结构仍具挑战。以往方法在平坦地面假设下将图像特征投影至鸟瞰图（BEV）空间，导致真实道路产生几何畸变。近期方法虽预测显式高程图以捕捉非平面表面，但仍依赖稀疏锚点回归，且仅将恢复的几何信息用于空间变换，未实现语义理解。为此，我们提出HSDF-Lane，将路面隐式建模为在密集采样3D特征体上的高度对齐符号距离场（HSDF）。通过可微渲染，HSDF联合生成精确高程图与表面对齐特征。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提取的车道存在先验注入变压器查询，实现几何结构与语义引导的耦合。在OpenLane基准上的大量实验表明，HSDF-Lane在3D车道检测与高程图估计方面均达到当前最优性能。
原文摘要 · Abstract (English)
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.
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