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

• URL: http://arxiv.org/abs/2606.31172v1
• Date: Tue, 30 Jun 2026 06:02:59 GMT
• Title: HSDF-Lane: Height-Aligned Signed Distance Field with Semantic Lane Prior for 3D Lane Detection
• Abstract summary: 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.<n>HSDF-Lane achieves state-of-the-art performance in both 3D lane detection and height map estimation.
• Score: 42.418429168532406
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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.

Related papers

• Surfsvr: 2D Surface Priors as 3D Geometric Regularizers for Sparse Voxel Reconstruction [56.51413081953813]
  SurfSVR is a novel sparse voxel reconstruction paradigm that treats 2D surface priors as explicit 3D geometric regularizers.<n>It consistently improves sparse voxel reconstruction across scenes with substantially different visibility and geometry characteristics.
  arXiv  Detail & Related papers  (2026-08-12T11:25:31Z)
• Height-Guided Projection Reparameterization for Camera-LiDAR Occupancy [19.450674475241353]
  HiPR is a camera-LiDAR occupancy framework with Height-Guided Projection Re parameterization.<n>HiPR consistently outperforms existing state-of-the-art methods while maintaining real-time inference.
  arXiv  Detail & Related papers  (2026-05-06T16:09:13Z)
• SpatialFly: Geometry-Guided Representation Alignment for UAV Vision-and-Language Navigation in Urban Environments [49.966170814478915]
  We propose a geometry-guided spatial representation framework for UAV VLN.<n>Running on RGB observations without explicit 3D reconstruction, SpatialFly introduces a geometry-guided 2D representation alignment mechanism.<n> Experimental results show that SpatialFly consistently outperforms state-of-the-art UAV VLN baselines across both seen and unseen environments.
  arXiv  Detail & Related papers  (2026-03-22T03:56:58Z)
• Robust Mesh Saliency GT Acquisition in VR via View Cone Sampling and Geometric Smoothing [59.12032628787018]
  3D mesh saliency ground truth is essential for human-centric visual modeling in virtual reality (VR)<n>Current VR eye-tracking pipelines rely on single ray sampling and Euclidean smoothing, triggering texture attention and signal leakage across gaps.<n>This paper proposes a robust framework to address these limitations.
  arXiv  Detail & Related papers  (2026-01-06T05:20:12Z)
• FreqPDE: Rethinking Positional Depth Embedding for Multi-View 3D Object Detection Transformers [91.59069344768858]
  We introduce Frequency-aware Positional Depth Embedding (FreqPDE) to equip 2D image features with spatial information for 3D detection transformer decoder.<n>FreqPDE combines the 2D image features and 3D position embeddings to generate 3D depth-aware features for query decoding.
  arXiv  Detail & Related papers  (2025-10-17T07:36:54Z)
• Implicit 3D scene reconstruction using deep learning towards efficient collision understanding in autonomous driving [0.0]
  This study develops a learning-based 3D scene reconstruction methodology that leverages LiDAR data and a deep neural network to build the static Signed Distance Function (SDF) maps.<n>Our preliminary results demonstrate that this method would significantly enhance collision detection performance, particularly in congested and dynamic environments.
  arXiv  Detail & Related papers  (2025-06-18T18:42:04Z)
• HeightLane: BEV Heightmap guided 3D Lane Detection [6.940660861207046]
  Accurate 3D lane detection from monocular images presents significant challenges due to depth ambiguity and imperfect ground modeling.<n>Our study introduces HeightLane, an innovative method that predicts a height map from monocular images by creating anchors based on a multi-slope assumption.<n>HeightLane achieves state-of-the-art performance in terms of F-score, highlighting its potential in real-world applications.
  arXiv  Detail & Related papers  (2024-08-15T17:14:57Z)
• LaneCPP: Continuous 3D Lane Detection using Physical Priors [45.52331418900137]
  Lane CPP uses a continuous 3D lane detection model leveraging physical prior knowledge about the lane structure and road geometry. We show the benefits of our contributions and prove the meaningfulness of using priors to make 3D lane detection more robust.
  arXiv  Detail & Related papers  (2024-06-12T16:31:06Z)
• LATR: 3D Lane Detection from Monocular Images with Transformer [42.34193673590758]
  3D lane detection from monocular images is a fundamental yet challenging task in autonomous driving. Recent advances rely on structural 3D surrogates built from front-view image features and camera parameters. We present a novel LATR model, an end-to-end 3D lane detector that uses 3D-aware front-view features without transformed view representation.
  arXiv  Detail & Related papers  (2023-08-08T21:08:42Z)
• Learning Anchored Unsigned Distance Functions with Gradient Direction Alignment for Single-view Garment Reconstruction [92.23666036481399]
  We propose a novel learnable Anchored Unsigned Distance Function (AnchorUDF) representation for 3D garment reconstruction from a single image. AnchorUDF represents 3D shapes by predicting unsigned distance fields (UDFs) to enable open garment surface modeling at arbitrary resolution.
  arXiv  Detail & Related papers  (2021-08-19T03:45:38Z)
• Road Curb Detection and Localization with Monocular Forward-view Vehicle Camera [74.45649274085447]
  We propose a robust method for estimating road curb 3D parameters using a calibrated monocular camera equipped with a fisheye lens. Our approach is able to estimate the vehicle to curb distance in real time with mean accuracy of more than 90%.
  arXiv  Detail & Related papers  (2020-02-28T00:24:18Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.