Real-time Non-line-of-sight Imaging with Two-step Deep Remapping

Conventional imaging only records the photons directly sent from the object to the detector, while non-line-of-sight (NLOS) imaging takes the indirect light into account. To explore the NLOS surroundings, most NLOS solutions employ a transient scanning process, followed by a back-projection based algorithm to reconstruct the NLOS scenes. However, the transient detection requires sophisticated apparatus, with long scanning time and low robustness to ambient environment, and the reconstruction algorithms typically cost tens of minutes with high demand on memory and computational resources. Here we propose a new NLOS solution to address the above defects, with innovations on both detection equipment and reconstruction algorithm. We apply inexpensive commercial Lidar for detection, with much higher scanning speed and better compatibility to real-world imaging tasks. Our reconstruction framework is deep learning based, consisting of a variational autoencoder and a compression neural network. The generative feature and the two-step reconstruction strategy of the framework guarantee high fidelity of NLOS imaging. The overall detection and reconstruction process allows for real-time responses, with state-of-the-art reconstruction performance. We have experimentally tested the proposed solution on both a synthetic dataset and real objects, and further demonstrated our method to be applicable for full-color NLOS imaging.