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arXiv · 2201.02475

Deep Domain Adversarial Adaptation for Photon-efficient Imaging

Yiwei Chen·Gongxin Yao·Yong Liu·Hongye Su·Xiaomin Hu·Yu Pan
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Abstract

Photon-efficient imaging with the single-photon light detection and ranging (LiDAR) captures the three-dimensional (3D) structure of a scene by only a few detected signal photons per pixel. However, the existing computational methods for photon-efficient imaging are pre-tuned on a restricted scenario or trained on simulated datasets. When applied to realistic scenarios whose signal-to-background ratios (SBR) and other hardware-specific properties differ from those of the original task, the model performance often significantly deteriorates. In this paper, we present a domain adversarial adaptation design to alleviate this domain shift problem by exploiting unlabeled real-world data, with significant resource savings. This method demonstrates superior performance on simulated and real-world experiments using our home-built up-conversion single-photon imaging system, which provides an efficient approach to bypass the lack of ground-truth depth information in implementing computational imaging algorithms for realistic applications.

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Yiwei Chen, Gongxin Yao, Yong Liu, Hongye Su, Xiaomin Hu, Yu Pan. 2022-01-07. Deep Domain Adversarial Adaptation for Photon-efficient Imaging. https://arxiv.org/abs/2201.02475

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