Shape-Preserving and Surface-Fitting Network for 3D Lane Detection

Current transformer-based 3D lane detection methods typically use instance activation maps (IAM) and point-to-point loss to achieve small geometric deviations of lanes. However, these methods suffer from such lane visibility issues as wrong lane extensions and lane omissions because IAM places the lane vanishing points by mistake and loses the blurred lanes. And their performance is limited by lane continuity issues while using the point-to-point loss. In this paper, we propose a shape-preserving and surface-fitting (SPSF) network to improve the lane visibility and enhance the lane continuity. The proposed SPSF network consists of three key steps: 3D lane preliminary prediction, lane shape-preserving, and 3D lane surface-fitting. First, we design a novel transformer decoder with a mask-guided denoising block to predict preliminary 3D lanes after generating 2D lane masks. Next, after mapping the preliminary 3D lanes to 2D projected lanes, lane shapes are preserved using a two-stage mask-guided strategy to avoid the visibility issues. The two-stage mask-guided strategy includes mask-directed horizontal position adjustment and visibility correction. Finally, after fitting the surface of 3D Lanes, we improve the continuity of lanes through a surface-fitting loss. Various experiments show that our work achieves SOTA performance on two standard benchmarks.