GroupLane: End-to-End 3D Lane Detection with Channel-wise Grouping
Zhuoling Li Chunrui Han Zheng Ge Jinrong Yang En Yu Haoqian Wang Hengshuang Zhao Xiangyu Zhang
Efficiency is quite important for 3D lane detection due to practical
deployment demand. In this work, we propose a simple, fast, and end-to-end
detector that still maintains high detection precision. Specifically, we devise
a set of fully convolutional heads based on row-wise classification. In
contrast to previous counterparts, ours supports recognizing both vertical and
horizontal lanes. Besides, our method is the first one to perform row-wise
classification in bird-eye-view. In the heads, we split feature into multiple
groups and every group of feature corresponds to a lane instance. During
training, the predictions are associated with lane labels using the proposed
single-win one-to-one matching to compute loss, and no post-processing
operation is demanded for inference. In this way, our proposed fully
convolutional detector, GroupLane, realizes end-to-end detection like DETR.
Evaluated on 3 real world 3D lane benchmarks, OpenLane, Once-3DLanes, and
OpenLane-Huawei, GroupLane adopting ConvNext-Base as the backbone outperforms
the published state-of-the-art PersFormer by 13.6% F1 score in the OpenLane
validation set. Besides, GroupLane with ResNet18 still surpasses PersFormer by
4.9% F1 score, while the inference speed is nearly 7x faster and the FLOPs is
only 13.3% of it.