3D Lane Detection from Front or Surround-View using Joint-Modeling &
  Matching
Haibin Zhou Jun Chang Tao Lu Huabing Zhou
3D lanes offer a more comprehensive understanding of the road surface
geometry than 2D lanes, thereby providing crucial references for driving
decisions and trajectory planning. While many efforts aim to improve prediction
accuracy, we recognize that an efficient network can bring results closer to
lane modeling. However, if the modeling data is imprecise, the results might
not accurately capture the real-world scenario. Therefore, accurate lane
modeling is essential to align prediction results closely with the environment.
This study centers on efficient and accurate lane modeling, proposing a joint
modeling approach that combines Bezier curves and interpolation methods.
Furthermore, based on this lane modeling approach, we developed a Global2Local
Lane Matching method with Bezier Control-Point and Key-Point, which serve as a
comprehensive solution that leverages hierarchical features with two
mathematical models to ensure a precise match. We also introduce a novel 3D
Spatial Constructor, representing an exploration of 3D surround-view lane
detection research. The framework is suitable for front-view or surround-view
3D lane detection. By directly outputting the key points of lanes in 3D space,
it overcomes the limitations of anchor-based methods, enabling accurate
prediction of closed-loop or U-shaped lanes and effective adaptation to complex
road conditions. This innovative method establishes a new benchmark in
front-view 3D lane detection on the Openlane dataset and achieves competitive
performance in surround-view 2D lane detection on the Argoverse2 dataset.