Passive NLOS imaging based on menifold embedding
Passive NLOS imaging is an extremely pathological problem. This project aims to complete data-driven passive NLOS imaging through deep learning. The proposed algorithm can make use of previously underutilized scene priors, thereby improving the effect of passive NLOS imaging.
We also collected a data set with more than 3,000,000 samples, hoping to alleviate the problem of insufficient data set faced by NLOS imaging. After all, the performance of the supervised algorithm depends to a large extent on the quality of the dataset.
This project has been submitted to a journal.