DEIO: Deep Event Inertial Odometry

Paper | Website | Demo

Abstract

Event cameras show great potential for visual odometry (VO) in handling challenging situations, such as fast motion and high dynamic range. Despite this promise, the sparse and motion-dependent characteristics of event data continue to limit the performance of feature-based or direct-based data association methods in practical applications. To address these limitations, we propose Deep Event Inertial Odometry (DEIO), the first monocular learning-based event-inertial framework, which combines a learning-based method with traditional nonlinear graph-based optimization. Specifically, an event-based recurrent network is adopted to provide accurate and sparse associations of event patches over time. DEIO further integrates it with the IMU to recover up-to-scale pose and provide robust state estimation. The Hessian information derived from the learned differentiable bundle adjustment (DBA) is utilized to optimize the co-visibility factor graph, which tightly incorporates event patch correspondences and IMU pre-integration within a keyframe-based sliding window. Comprehensive validations demonstrate that DEIO achieves superior performance on 10 challenging public benchmarks compared with more than 20 state-of-the-art methods.

Update log

• README Upload (2024/10/28)
• Paper Upload (2024/11/06)
• Estimated Trajectories Upload (2024/11/07)
• Code Upload (2025/07/19)
• More Raw Results of VECtor Dataset (2025/07/20)

Setup and Installation

# for cuda 11.7
conda env create -f environment.yml   
conda activate DEIO
# conda remove --name DEIO --all

pip install .
pip install numpy-quaternion==2022.4.3

# install GTSAM
cd thirdparty/gtsam
mkdir build
cd build
cmake .. -DGTSAM_BUILD_PYTHON=1 -DGTSAM_PYTHON_VERSION=3.10.11
make python-install

Run an Example

conda activate DEIO

# example for the davia240c
CUDA_VISIBLE_DEVICES=0 PYTHONPATH={YOUR_PATH}/DEIO python script/eval_deio/davis240c.py \
    --inputdir=/media/lfl-data2/davis240c \
    --config=config/davis240c.yaml \
    --val_split=script/splits/davis240c/davis240c_val.txt \
    --enable_event \
    --network={YOUR_PATH}/DEVO/DEVO.pth  \
    --plot \
    --save_trajectory \
    --trials=5

The Results will save in path: results.

Using Our Results as Comparison

For the convenience of the comparison, we release the estimated trajectories of DEIO in tum format in the dir of estimated_trajectories. What's more, we also give the sample code for the quantitative and qualitative evaluation using evo package

• DAVIS240c
• Mono-HKU
• Stereo-HKU
• UZH-PFV
• VECtor
• MVSEC
• EDS
• TUM-VIE
• DSEC

Acknowledgement

• This work is based on DPVO, DEVO, DROID-SLAM, DBA-Fusion, and GTSAM
• If you find this work is helpful in your research, a simple star or citation of our works should be the best affirmation for us. 😊

@inproceedings{GWPHKU:DEIO,
    title={DEIO: Deep Event Inertial Odometry},
    author={Guan, Weipeng and Lin, Fuling and Chen, Peiyu and Lu, Peng},
    booktitle={Proceedings of the IEEE/CVF International Conference on Computer Vision Workshops},
    year={2025}
  }