# Asynchronous Single-Photon 3D Imaging

arXiv: https://arxiv.org/abs/1908.06372 · HTML: https://mlsanity.com/paper/1908.06372/

## Key facts

- Cited by 25 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 18 Aug 2019.

## Built on by (most cited first, 25 of 25)

- [Passive Inter-Photon Imaging](https://mlsanity.com/paper/2104.00059/) (2021)
- [Progressive Joint Low-light Enhancement and Noise Removal for Raw Images](https://mlsanity.com/paper/2106.14844/) (2021)
- [iToF2dToF: A Robust and Flexible Representation for Data-Driven Time-of-Flight Imaging](https://mlsanity.com/paper/2103.07087/) (2021)
- [Photon-Starved Scene Inference using Single Photon Cameras](https://mlsanity.com/paper/2107.11001/) (2021)
- [High-speed object detection with a single-photon time-of-flight image sensor](https://mlsanity.com/paper/2107.13407/) (2021)
- [Adaptive Gating for Single-Photon 3D Imaging](https://mlsanity.com/paper/2111.15047/) (2021)
- [3D Scene Inference from Transient Histograms](https://mlsanity.com/paper/2211.05094/) (2022)
- [Event Neural Networks](https://mlsanity.com/paper/2112.00891/) (2021)
- [Count-Free Single-Photon 3D Imaging with Race Logic](https://mlsanity.com/paper/2307.04924/) (2023)
- [Unlocking the Performance of Proximity Sensors by Utilizing Transient Histograms](https://mlsanity.com/paper/2308.13473/) (2023)
- [Time-Resolved MNIST Dataset for Single-Photon Recognition](https://mlsanity.com/paper/2410.16744/) (2024)
- [Resolution Limit of Single-Photon LiDAR](https://mlsanity.com/paper/2403.17719/) (2024)
- [Single-Photon 3D Imaging with Equi-Depth Photon Histograms](https://mlsanity.com/paper/2408.16150/) (2024)
- [Robust 3D Object Detection using Probabilistic Point Clouds from Single-Photon LiDARs](https://mlsanity.com/paper/2508.00169/) (2025)
- [Swept-Angle Synthetic Wavelength Interferometry](https://mlsanity.com/paper/2205.10655/) (2022)
- [Passive Micron-scale Time-of-Flight with Sunlight Interferometry](https://mlsanity.com/paper/2211.10732/) (2022)
- [Single-photon Image Super-resolution via Self-supervised Learning](https://mlsanity.com/paper/2303.02033/) (2023)
- [Generative Quanta Color Imaging](https://mlsanity.com/paper/2403.19066/) (2024)
- [Analysis and Improvement of Rank-Ordered Mean Algorithm in Single-Photon LiDAR](https://mlsanity.com/paper/2407.20399/) (2024)
- [Shoot-Bounce-3D: Single-Shot Occlusion-Aware 3D from Lidar by Decomposing Two-Bounce Light](https://mlsanity.com/paper/2512.06080/) (2025)
- [Centimeter-Wave Free-Space Time-of-Flight Imaging](https://mlsanity.com/paper/2105.11606/) (2021)
- [Hardware-aware Coding Function Design for Compressive Single-Photon 3D Cameras](https://mlsanity.com/paper/2510.12123/) (2025)
- [Accurate Simulation Pipeline for Passive Single-Photon Imaging](https://mlsanity.com/paper/2601.12850/) (2026)
- [Learning to Suppress SPAD-based LiDAR Flare](https://mlsanity.com/paper/2607.03247/) (2026)
- [High-Flux Count-Free Single-Photon 3D Cameras](https://mlsanity.com/paper/2608.18306/) (2026)

## Abstract (arXiv)

Single-photon avalanche diodes (SPADs) are becoming popular in time-of-flight depth-ranging due to their unique ability to capture individual photons with picosecond timing resolution. However, ambient light (e.g., sunlight) incident on a SPAD-based 3D camera leads to severe non-linear distortions (pileup) in the measured waveform, resulting in large depth errors. We propose asynchronous single-photon 3D imaging, a family of acquisition schemes to mitigate pileup during data acquisition itself. Asynchronous acquisition temporally misaligns SPAD measurement windows and the laser cycles through deterministically predefined or randomized offsets. Our key insight is that pileup distortions can be "averaged out" by choosing a sequence of offsets that span the entire depth range. We develop a generalized image formation model and perform theoretical analysis to explore the space of asynchronous acquisition schemes and design high-performance schemes. Our simulations and experiments demonstrate an improvement in depth accuracy of up to an order of magnitude as compared to the state-of-the-art, across a wide range of imaging scenarios, including those with high ambient flux.