# Deep Domain Adversarial Adaptation for Photon-efficient Imaging

arXiv: https://arxiv.org/abs/2201.02475 · HTML: https://mlsanity.com/paper/2201.02475/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 7 of its 28 references that are in the map.
- First posted on arXiv 7 Jan 2022.

## Builds on (most cited first, 7 of 7)

- [Domain-Adversarial Training of Neural Networks](https://mlsanity.com/paper/1505.07818/) (2015)
- [Adversarial Discriminative Domain Adaptation](https://mlsanity.com/paper/1702.05464/) (2017)
- [A Comprehensive Survey on Transfer Learning](https://mlsanity.com/paper/1911.02685/) (2019)
- [Learning to Adapt Structured Output Space for Semantic Segmentation](https://mlsanity.com/paper/1802.10349/) (2018)
- [Single-photon computational 3D imaging at 45 km](https://mlsanity.com/paper/1904.10341/) (2019)
- [Robust photon-efficient imaging using a pixel-wise residual shrinkage network](https://mlsanity.com/paper/2201.01453/) (2022)
- [Non-line-of-sight 3D imaging with a single-pixel camera](https://mlsanity.com/paper/1903.04812/) (2019)

## Built on by (most cited first, 2 of 2)

- [Single-photon Image Super-resolution via Self-supervised Learning](https://mlsanity.com/paper/2303.02033/) (2023)
- [Point upsampling networks for single-photon sensing](https://mlsanity.com/paper/2508.12986/) (2025)

## Abstract (arXiv)

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.