CGNet

The official implementation of the ECCV 2024 paper: Continuity Preserving Online CenterLine Graph Learning

Abstract

Lane topology, which is usually modeled by a centerline graph, is essential for high-level autonomous driving. For a high-quality graph, both topology connectivity and spatial continuity of centerline segments are critical. However, most of existing approaches pay more attention to connectivity while neglect the continuity. Such kind of cen- terline graph usually cause problem to planning of autonomous driving. To overcome this problem, we present an end-to-end network, CGNet, with three key modules: 1) Junction Aware Query Enhancement module, which provides positional prior to accurately predict junction points; 2) Bézier Space Connection module, which enforces continuity constraints on any two topologically connected segments in a Bézier space; 3) It- erative Topology Refinement module, which is a graph-based network with memory to iteratively refine the predicted topological connectivity. CGNet achieves state-of-the-art performance on both nuScenes and Ar- goverse2 datasets

Motivation

Top: A toy example which illustrates the centerline graph and the impact of overlooking the continuity. Bottom: Comparison with MapTR and TopoNet. They predicts inaccurate position of junction points and wrong topology, all leading to the discontinuous path. Our CGNet obtain the continuous path.

Qualitative results

Qualitative comparisons under different weather and lighting conditions on nuScenes. CGNet predicts more accurate position of junction points and correct topology, leading to a more continuous and smooth path.

Usage

Download

Download the pretrained models using these link: pretrained_models.

Installation

conda create -n cgnet-env python=3.8 -y
pip install -r requirement.txt

cd mmdetection3d
python setup.py develop

Prepare nuScenes data

python tools/create_data.py nuscenes --root-path ./data/nuscenes --out-dir ./data/nuscenes/ann --extra-tag nuscenes --version v1.0 --canbus ./data/nuscenes

Train, Test and Visualize

#train
python tools/train.py projects/configs/cgnet/cgnet_ep110.py

#test
python tools/test.py projects/configs/cgnet/cgnet_ep110.py ckpts/cgnet_ep110.pth --eval chamfer openlane topology

#vis
python tools/CGNet_visualize.py projects/configs/test/cgnet_local.py ckpts/cgnet_ep110.pth --show-dir ./show

Citation

If you find this work useful for your research, please cite:

@misc{han2024continuitypreservingonlinecenterline,
      title={Continuity Preserving Online CenterLine Graph Learning}, 
      author={Yunhui Han and Kun Yu and Zhiwei Li},
      year={2024},
      eprint={2407.11337},
      archivePrefix={arXiv},
      primaryClass={cs.CV},
      url={https://arxiv.org/abs/2407.11337}, 
}

Acknowledgements

We would like to thank MapTR, STSU, LaneGNN, OpenLane-V2, TopoNet, VectorMapNet for their great codes!