HSDF-Lane: Height-Aligned Signed Distance Field with Semantic Lane Prior for 3D Lane Detection

Monocular 3D lane detection plays a critical role in autonomous driv-ing, yet recovering reliable 3D geometry from a single image remains challeng-ing due to inherent depth ambiguity. Prior methods project image features intoBird’s-Eye-View (BEV) space under a flat-ground assumption, causing geomet-ric distortion on real-world roads. Recent methods instead predict explicit heightmaps to capture non-planar surfaces, but still rely on sparse anchor-based regres-sion and exploit the recovered geometry merely for spatial transformation ratherthan semantic understanding. To overcome these limitations, we propose HSDF-Lane, which implicitly models the road surface as a Height-aligned Signed Dis-tance Field (HSDF) over a densely sampled 3D feature volume. Through dif-ferentiable rendering, the HSDF jointly produces an accurate height map andsurface-aligned features. We further introduce Lane-aware Semantic PositionalEncoding (LSPE), which injects a lane-existence prior derived from the surface-aligned features into the transformer queries, coupling geometric structure withsemantic guidance. Extensive experiments on the OpenLane benchmark showthat HSDF-Lane achieves state-of-the-art performance in both 3D lane detectionand height map estimation. The code is available at https://github.com/JiyongBoo/HSDF-Lane.