# High-Flux Count-Free Single-Photon 3D Cameras

arXiv: https://arxiv.org/abs/2608.18306 · HTML: https://mlsanity.com/paper/2608.18306/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 13 of its 42 references that are in the map.
- First posted on arXiv 18 Aug 2026.

## Builds on (most cited first, 13 of 13)

- [Asynchronous Single-Photon 3D Imaging](https://mlsanity.com/paper/1908.06372/) (2019)
- [High Flux Passive Imaging with Single-Photon Sensors](https://mlsanity.com/paper/1902.10190/) (2019)
- [Photon-Flooded Single-Photon 3D Cameras](https://mlsanity.com/paper/1903.08347/) (2019)
- [iToF2dToF: A Robust and Flexible Representation for Data-Driven Time-of-Flight Imaging](https://mlsanity.com/paper/2103.07087/) (2021)
- [Dead Time Compensation for High-Flux Ranging](https://mlsanity.com/paper/1810.11145/) (2018)
- [Adaptive Gating for Single-Photon 3D Imaging](https://mlsanity.com/paper/2111.15047/) (2021)
- [A Sketching Framework for Reduced Data Transfer in Photon Counting Lidar](https://mlsanity.com/paper/2102.08732/) (2021)
- [Count-Free Single-Photon 3D Imaging with Race Logic](https://mlsanity.com/paper/2307.04924/) (2023)
- [Spline Sketches: An Efficient Approach for Photon Counting Lidar](https://mlsanity.com/paper/2210.07314/) (2022)
- [Histogram-less LiDAR through SPAD response linearization](https://mlsanity.com/paper/2310.09176/) (2023)
- [Single-Photon 3D Imaging with Equi-Depth Photon Histograms](https://mlsanity.com/paper/2408.16150/) (2024)
- [Sketched RT3D: How to reconstruct billions of photons per second](https://mlsanity.com/paper/2203.00952/) (2022)
- [Free-running vs. Synchronous: Single-Photon Lidar for High-flux 3D Imaging](https://mlsanity.com/paper/2507.09386/) (2025)

## Abstract (arXiv)

Single-photon cameras based on single-photon avalanche diode (SPAD) technology are gaining popularity for 3D sensing, thanks to their extreme sensitivity and time resolution. There are two key challenges with single-photon cameras that limit their widespread use: (i) they suffer from non-linear distortions called ''pile-up'' when operated in high-photon-flux conditions, and (ii) they generate a large volume of raw photon data, creating a severe data bottleneck at each sensor pixel. In this work, we show that while compressive capture techniques successfully mitigate data transfer challenges, they exacerbate the effects of dead-time distortion because they fail to retain sufficient information about the photon detection history to allow post-processing pile-up correction via existing methods. We propose a new computational-imaging method that combines free-running capture with an analysis-by-synthesis software pipeline to mitigate pile-up distortions. Our results with hardware emulations and full-scene and single-pixel simulations show that our method can reliably capture scene distance and reflectance over a wide range of illumination conditions. Our work will enable high-resolution SPAD cameras that are severely bandwidth-constrained to operate in real-world high-flux scenarios.