@inproceedings{
cui2024virtual,
title={Virtual Scanning: Unsupervised Non-line-of-sight Imaging from Irregularly Undersampled Transients},
author={Xingyu Cui and Huanjing Yue and Song Li and Xiangjun Yin and Yusen Hou and Yun Meng and Kai Zou and Xiaolong Hu and Jingyu Yang},
booktitle={The Thirty-eighth Annual Conference on Neural Information Processing Systems},
year={2024},
url={https://openreview.net/forum?id=R4IBZrSF5d}
}
Non-line-of-sight (NLOS) imaging allows for seeing hidden scenes around corners through active sensing.
Most previous algorithms for NLOS reconstruction require dense transients acquired through regular scans over a large relay surface, which limits their applicability in realistic scenarios with irregular relay surfaces.
In this paper, we propose an unsupervised learning-based framework for NLOS imaging from irregularly undersampled transients~(IUT).
Our method learns implicit priors from noisy irregularly undersampled transients without requiring paired data, which is difficult and expensive to acquire and align. 
To overcome the ambiguity of the measurement consistency constraint in inferring the albedo volume, we design a virtual scanning process that enables the network to learn within both range and null spaces for high-quality reconstruction.
We devise a physics-guided SURE-based denoiser to enhance robustness to ubiquitous noise in low-photon imaging conditions. 
Extensive experiments on both simulated and real-world data validate the performance and generalization of our method.
Compared with the state-of-the-art (SOTA) method, our method achieves higher fidelity, greater robustness, and remarkably faster inference times by orders of magnitude.
The code and model are available at https://github.com/XingyuCuii/Virtual-Scanning-NLOS.
Virtual Scanning: Unsupervised Non-line-of-sight Imaging from Irregularly Undersampled Transients
Xingyu Cui Huanjing Yue Song Li Xiangjun Yin Yusen Hou Yun Meng Kai Zou Xiaolong Hu Jingyu Yang