D³L: Curvature-Constrained Denoising Diffusion Model for 3D Lane Detection

Monocular 3D lane detection is a challenging task for autonomous driving systems. Recent advances primarily focus on one-step methods for lane detection based on front-view features, which show promising results on straight lanes. However, curved lanes are difficult to handle with one-step prediction, which performs prediction in a single leap without gradual refinement. To address this issue, we propose a novel Denoising Diffusion Model for 3D Lane Detection framework (D³L). The main idea is to leverage the progressive generation capability of the diffusion model to generate accurate 3D curved lanes, and ensuring lane continuity through curvature constraints. The framework includes three creative components: coarse-to-fine denoiser (CFD), curvature-constrained loss (CCL) and multi-sampling aggregation strategy (MSAS). In CFD, both lane-level and point-level transformer blocks are integrated to accurately denoise 3D lanes, which effectively captures both global and local features. CCL is designed to reduce deviations in lane curvature, resulting in smoother lane continuity. This loss enhances both the accuracy and geometric consistency of lane detection, especially in complex curved scenes. MSAS is proposed to select the optimal lane point-by-point from multiple candidates, thus robustness of the lane prediction is significantly improved. Extensive experiments on two popular 3D lane detection benchmarks demonstrate that our D³ L outperforms the state-of-the-art methods.