CurveFormer: 3D Lane Detection by Curve Propagation with Curve Queries and Attention

• URL: http://arxiv.org/abs/2209.07989v1
• Date: Fri, 16 Sep 2022 14:54:57 GMT
• Title: CurveFormer: 3D Lane Detection by Curve Propagation with Curve Queries and Attention
• Authors: Yifeng Bai, Zhirong Chen, Zhangjie Fu, Lang Peng, Pengpeng Liang, Erkang Cheng
• Abstract summary: 3D lane detection is an integral part of autonomous driving systems. Previous CNN and Transformer-based methods usually first generate a bird's-eye-view (BEV) feature map from the front view image. We propose CurveFormer, a single-stage Transformer-based method that directly calculates 3D lane parameters.
• Score: 3.330270927081078
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 3D lane detection is an integral part of autonomous driving systems. Previous CNN and Transformer-based methods usually first generate a bird's-eye-view (BEV) feature map from the front view image, and then use a sub-network with BEV feature map as input to predict 3D lanes. Such approaches require an explicit view transformation between BEV and front view, which itself is still a challenging problem. In this paper, we propose CurveFormer, a single-stage Transformer-based method that directly calculates 3D lane parameters and can circumvent the difficult view transformation step. Specifically, we formulate 3D lane detection as a curve propagation problem by using curve queries. A 3D lane query is represented by a dynamic and ordered anchor point set. In this way, queries with curve representation in Transformer decoder iteratively refine the 3D lane detection results. Moreover, a curve cross-attention module is introduced to compute the similarities between curve queries and image features. Additionally, a context sampling module that can capture more relative image features of a curve query is provided to further boost the 3D lane detection performance. We evaluate our method for 3D lane detection on both synthetic and real-world datasets, and the experimental results show that our method achieves promising performance compared with the state-of-the-art approaches. The effectiveness of each component is validated via ablation studies as well.

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