Awesome
E-3DTrack
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
- 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
- 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
- Our E-3DTrack dataset could be downloaded from https://github.com/lisiqi19971013/event-based-datasets.
- 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
- 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"
- 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},
}