Awesome

TEF

Introduction

pipeline This repo contains the code for High-fidelity Event-Radiance Recovery via Transient Event Frequency (CVPR 2023 [Paper], [Video]), by Jin Han, Yuta Asano, Boxin Shi, Yinqiang Zheng, and Imari Sato.

How to run the code

We provide data samples in this link. Please download the data samples and move them into the folder ./data_samples/. The recovered radiance values will be saved in ./data_samples/xxx/ev_radiance_360x640_len4.npy.

For hyperspectral reconstruction, since there are multiple event files captured under different light sources (with different narrow-band wavelengths), it takes longer time to get the radiance values in different wavelengths.

For hyperspectral reconstruction:

python TEF.py -m hyperspectral -i data_samples/painting

Then relight using different lighting files:

python relight.py -i data_samples/painting

For depth sensing:

python TEF.py -m depth -i data_samples/depth

For iso-depth contour reconstruction:

python TEF.py -m iso-contour -i data_samples/shape

Citation

If you find the paper is useful for your research, please cite our paper as follows:

@InProceedings{Han_2023_CVPR,
    author    = {Han, Jin and Asano, Yuta and Shi, Boxin and Zheng, Yinqiang and Sato, Imari},
    title     = {High-Fidelity Event-Radiance Recovery via Transient Event Frequency},
    booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
    month     = {June},
    year      = {2023},
    pages     = {20616-20625}
}