E-3DTrack

August 21, 2025 ยท View on GitHub

This repository is for the CVPR 2024 paper "3D Feature Tracking via Event Camera". This work is inspired by the great success of "Data-Driven Feature Tracking for Event Cameras" (CVPR 2023 Best Paper Candidate) and extends it to 3D.

Requirements

  1. Python 3.8 with the following packages installed:
    • einops==0.4.1
    • kornia==0.6.7
    • opencv-python==4.6.0.66
    • torch==1.9.0
    • tqdm==4.64.0
  2. cuda
    • CUDA enabled GPUs are required for training. We train our code with CUDA 11.1 V11.1.105 on A100 GPUs and test on NVIDIA 3090 GPUs.

Data preparing

  1. Our E-3DTrack dataset could be downloaded from https://github.com/lisiqi19971013/event-based-datasets.
  2. Download the pre-trained model from https://drive.google.com/file/d/1Gx0zhIeciHGEqrRryPmAC-mqoNO1wuMQ/view?usp=sharing or from https://pan.baidu.com/s/1ONvkUyk2cqWM2XR_XwaKeg (extract code: 2024).

Evaluation

  1. Modify the variables "ckpt_path" and "data_folder" in the file "eval.py" accordingly.

Run the following code to generate output results.

>>> python eval.py

The output predictions could be found at "./output"

  1. Calculate metrics using the following code.
>>> python calMetric.py

Citation

@inproceedings{e3dtrack,
    title={3D Feature Tracking via Event Camera}, 
    author={Li, Siqi and Zhou, Zhikuan and Xue Zhou and Li, Yipeng and Du, Shaoyi and Gao, Yue},
    booktitle={Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition}, 
    year={2024},
}