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TopoNet: A New Baseline for Scene Topology Reasoning
This reporsitory will contain the source code of TopoNet from the paper, Topology Reasoning for Driving Scenes.
TopoNet is the first end-to-end framework capable of abstracting traffic knowledge beyond conventional perception tasks, ie., reasoning connections between centerlines and traffic elements from sensor inputs. It unifies heterogeneous feature learning and enhances feature interactions via the graph neural network architecture and the knowledge graph design.
Topology Reasoning for Driving Scenes
Tianyu Li*, Li Chen*†, Xiangwei Geng, Huijie Wang, Yang Li, Zhenbo Liu, Shengyin Jiang, Yuting Wang, Hang Xu, Chunjing Xu, Feng Wen, Ping Luo, Junchi Yan, Wei Zhang, Xiaogang Wang, Yu Qiao, Hongyang Li†.
Paper: Full paper on arXiv
News
- Code & model will be released around June. Please stay tuned!
- [2023/4/11] TopoNet paper is available on arXiv.
Main Results
We provide results on Openlane-V2. Models will be released together with codes.
| Method | Backbone | Epoch | DETl | DETl,chamfer | TOPll | DETt | TOPlt | OLS | Model |
|---|---|---|---|---|---|---|---|---|---|
| STSU | ResNet-50 | 24 | 12.0 | 11.5 | 0.3 | 62.3 | 10.1 | 27.9 | - |
| VectorMapNet | ResNet-50 | 24 | 11.3 | 13.4 | 0.1 | 58.5 | 6.2 | 24.5 | - |
| MapTR | ResNet-50 | 24 | 8.3 | 17.7 | 0.2 | 60.7 | 5.8 | 24.3 | - |
| MapTR* | ResNet-50 | 24 | 8.3 | 17.7 | 1.1 | 60.7 | 10.1 | 30.2 | - |
| TopoNet | ResNet-50 | 24 | 22.1 | 20.2 | 2.7 | 59.1 | 14.9 | 34.0 | - |
$*$ : evaluation based on matching results on Chamfer distance.
License
All assets and code are under the Apache 2.0 license unless specified otherwise.
Citation
If this work is helpful for your research, please consider citing the following BibTeX entry.
@article{li2023toponet,
title={Topology Reasoning for Driving Scenes},
author={Li, Tianyu and Chen, Li and Geng, Xiangwei and Wang, Huijie and Li, Yang and Liu, Zhenbo and Jiang, Shengyin and Wang, Yuting and Xu, Hang and Xu, Chunjing and Wen, Feng and Luo, Ping and Yan, Junchi and Zhang, Wei and Wang, Xiaogang and Qiao, Yu and Li, Hongyang},
journal={arXiv preprint arXiv:2304.05277},
year={2023}
}Related resources
We acknowledge all the open source contributors for the following projects to make this work possible: