  In autonomous driving, 3D lane detection using monocular cameras is an
important task for various downstream planning and control tasks. Recent CNN
and Transformer approaches usually apply a two-stage scheme in the model
design. The first stage transforms the image feature from a front image into a
bird's-eye-view (BEV) representation. Subsequently, a sub-network processes the
BEV feature map to generate the 3D detection results. However, these approaches
heavily rely on a challenging image feature transformation module from a
perspective view to a BEV representation. In our work, we present
CurveFormer++, a single-stage Transformer-based method that does not require
the image feature view transform module and directly infers 3D lane detection
results from the perspective image features. Specifically, our approach models
the 3D detection task as a curve propagation problem, where each lane is
represented by a curve query with a dynamic and ordered anchor point set. By
employing a Transformer decoder, the model can iteratively refine the 3D lane
detection results. A curve cross-attention module is introduced in the
Transformer decoder to calculate similarities between image features and curve
queries of lanes. To handle varying lane lengths, we employ context sampling
and anchor point restriction techniques to compute more relevant image features
for a curve query. Furthermore, we apply a temporal fusion module that
incorporates selected informative sparse curve queries and their corresponding
anchor point sets to leverage historical lane information. In the experiments,
we evaluate our approach for the 3D lane detection task on two publicly
available real-world datasets. The results demonstrate that our method provides
outstanding performance compared with both CNN and Transformer based methods.
We also conduct ablation studies to analyze the impact of each component in our
approach.
