This repository is the modification of EVREAL (Event-based Video Reconstruction Evaluation and Analysis Library) by intergrating our family of CISTA networks, which includes CISTA-TC, CISTA-LSTC and CISTA-Flow. The original repository for CISTA-TC and CISTA-LSTC is V2E2V. Furthermore, we have made some changes for our evaluation, as described in CISTA-Flow. The main modifications are listed below:

We added code for our CISTA family networks in CISTAFlow/e2v/e2v_model.py, including CISTA, CISTA-TC, CISTA-LSTC and CISTA-Flow. The DCEIFlowCistaNet and ERAFTCistaNet are both CISTA-Flow networks that integrate DCEIFlow and ERAFT into CISTA-LSTC for motion compensation, respectively. In the evaluation, these two networks are referred to as CISTA-EIFlow and CISTA-ERAFT.

The pretrained models are located under pretrained. Note that CISTA-LSTC has three checkpoints: cista-lstc-old.pth.tar, cista-lstc.pth.tar, cista-lstc-randNE.pth.tar. CISTA-EIFlow has two checkpoints: cista-eiflow.pth.tar and cista-lstc-randNE.pth.tar. Details please refer to pretrained/CISTA-LSTC/readme.md and pretrained/CISTA-EIFlow/readme.md.

The cista-lstc.pth.tar checkpoint is used in the CISTA-Flow paper, while the old version is used in the TPAMI paper and the V2E2V repository. The training datasets and strategies are slightly different for these two checkpoints. Note that the new version is not necessarily better than the old one.

In addition, we have included pretrained models for E2VID, FireNet, and SPADE-E2VID, labeled as myE2VID, myFireNet and mySPADE.

We have added a new evaluation method: low_k_events. If the target number of events per reconstruction is $k$, We either divide events between two consecutive frames by $k$ into multiple groups or combine events across several frames into a single group for the reconstruction and flow estimation. Related configuration is in config/eval/low_k_events.json.

"voxel_method": {
            "method": "low_k_events",
            "k": 15000
        },

We have added PSNR and forward warping loss (FWL) in evaluation. The FWL is only used for optical flow estimation of CISTA-Flow when no ground truth flow is available.

save_interval: int: save data (frame / events / flow) every save_interval frames

save_events: bool : is save corresponding event frames

For detailed usage, please refer to EVREAL.

Example:

python eval.py \
-m CISTA-TC CISTA-LSTC CISTA-EIFlow CISTA-ERAFT \
-c low_k_events \
-d ECD HQF \
-qm mse psnr ssim lpips \

Related open-source repositories:

• V2E2V
• DCEIFlow
• ERAFT
• EVREAL
• event_cnn_minimal

We also provided codes for generating training datasets for video-to-events reconstruction:

• V2E_generation

If you use this library, please cite the following:

  @inproceedings{ercan2023evreal,
    title={{EVREAL}: Towards a Comprehensive Benchmark and Analysis Suite for Event-based Video Reconstruction},
    author={Ercan, Burak and Eker, Onur and Erdem, Aykut and Erdem, Erkut},
    booktitle={Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) Workshops},
    month={June},
    year={2023},
    pages={3942-3951}}

If you use the family of CISTA networks, please cite the publications as follows:

  @misc{liu2024enhanced,
    title={Enhanced Event-Based Video Reconstruction with Motion Compensation}, 
    author={Siying Liu and Pier Luigi Dragotti},
    year={2024},
    eprint={2403.11961},
    archivePrefix={arXiv},
    primaryClass={cs.CV}
}

  @article{liu_sensing_2023,  
    title={Sensing Diversity and Sparsity Models for Event Generation and Video Reconstruction from Events},   
    author={Liu, Siying and Dragotti, Pier Luigi},  
    journal={IEEE Transactions on Pattern Analysis and Machine Intelligence},  
    year={2023},  
    pages={1-16},  
    publisher={IEEE}. 
    doi={10.1109/TPAMI.2023.3278940}. 
    }

  @inproceedings{liu_convolutional_2022,  
    title={Convolutional ISTA Network with Temporal Consistency Constraints for Video Reconstruction from Event Cameras},  
    author={Liu, Siying and Alexandru, Roxana and Dragotti, Pier Luigi},  
    booktitle={ICASSP 2022-2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)},  
    pages={1935--1939},  
    year={2022},  
    organization={IEEE}. 
    doi={10.1109/ICASSP43922.2022.9746331}. 
    }