TopoPoint: Enhance Topology Reasoning via Endpoint Detection in Autonomous Driving

• Production from Institute of Computing Technology, Chinese Academy of Sciences.
• Primary contact: Yanping Fu ( fuyanping23s@ict.ac.cn ).

TL;DR

This repository contains the source code of TopoPoint: Enhance Topology Reasoning via Endpoint Detection in Autonomous Driving.

TopoPoint identify the endpoint deviation issue in existing topology reasoning methods. To tackle this, TopoPoint introduces explicit endpoint detection and strengthens point-lane interaction through Point-Lane Merge Self-Attention and Point-Lane Graph Convolutional Network, and further design Point-Lane Geometry Matching algorithm to refine lane endpoints.

Updates

• [2025.9.19] TopoPoint is accepted by NeurIPS 2025.
• [2025.5.26] TopoPoint paper is released at arXiv.

Table of Contents

• TopoPoint: Enhance Topology Reasoning via Endpoint Detection in Autonomous Driving
  • Table of Contents
  • Main Results
    • Results on OpenLane-V2 subset-A val
  • Prerequisites
  • Installation
  • Prepare Dataset
  • Train and Evaluate
    • Train
    • Evaluate
  • Citation
  • Related resources

Main Results

The result is based on the updated v2.1.0 OpenLane-V2 devkit and metrics.

Results on OpenLane-V2 subset-A val

We provide results on OpenLane-V2 subset-A val set.

Method	Backbone	Epoch	DETl	DETt	TOPll	TOPlt	OLS	DETp
STSU	ResNet-50	24	12.7	43.0	2.9	19.8	29.3	-
VectorMapNet	ResNet-50	24	11.1	41.7	2.7	9.2	24.9	-
MapTR	ResNet-50	24	17.7	43.5	5.9	15.1	31.0	-
TopoNet	ResNet-50	24	28.6	48.6	10.9	23.8	39.8	43.8
TopoMLP	ResNet-50	24	28.3	49.5	21.6	26.9	44.1	43.4
TopoLogic	ResNet-50	24	29.9	47.2	23.9	25.4	44.1	45.2
TopoFormer	ResNet-50	24	34.7	48.2	24.1	29.5	46.3	-
TopoPoint	ResNet-50	24	31.4	55.3	28.7	30.0	48.8	52.6

Prerequisites

• Linux
• Python 3.8.x
• NVIDIA GPU + CUDA 11.1
• PyTorch 1.9.1

Installation

We recommend using conda to run the code.

conda create -n topopoint python=3.8 -y
conda activate topopoint

# (optional) If you have CUDA installed on your computer, skip this step.
conda install cudatoolkit=11.1.1 -c conda-forge

pip install torch==1.9.1+cu111 torchvision==0.10.1+cu111 -f https://download.pytorch.org/whl/torch_stable.html

Install other required packages.

pip install -r requirements.txt

Prepare Dataset

Following OpenLane-V2 repo to download the data and run the preprocessing code.

Train and Evaluate

Train

We recommend using 8 GPUs for training. If a different number of GPUs is utilized, you can enhance performance by configuring the --autoscale-lr option. The training logs will be saved to work_dirs/[work_dir_name].

cd TopoPoint
mkdir work_dirs

./tools/dist_train.sh 8 [work_dir_name] [--autoscale-lr]

Evaluate

You can set --show to visualize the results.

./tools/dist_test.sh 8 [work_dir_name] [--show]

Citation

If this work is helpful for your research, please consider citing the following BibTeX entry.

@misc{fu2025topopoint,
      title={TopoPoint: Enhance Topology Reasoning via Endpoint Detection in Autonomous Driving}, 
      author={Yanping Fu and Xinyuan Liu and Tianyu Li and Yike Ma and Yucheng Zhang and Feng Dai},
      year={2025},
      eprint={2505.17771},
      archivePrefix={arXiv},
      primaryClass={cs.CV},
      url={https://arxiv.org/abs/2505.17771}, 
}

@inproceedings{fu2024topologic,
 author = {Fu, Yanping and Liao, Wenbin and Liu, Xinyuan and Xu, Hang and Ma, Yike and Zhang, Yucheng and Dai, Feng},
 booktitle = {Advances in Neural Information Processing Systems},
 pages = {61658--61676},
 title = {TopoLogic: An Interpretable  Pipeline for Lane Topology Reasoning on Driving Scenes},
 volume = {37},
 year = {2024}
}

Related resources

We acknowledge all the open-source contributors for the following projects to make this work possible:

• TopoLogic
• TopoNet
• Openlane-V2