# Count-Free Single-Photon 3D Imaging with Race Logic

arXiv: https://arxiv.org/abs/2307.04924 · HTML: https://mlsanity.com/paper/2307.04924/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- Builds on 8 of its 51 references that are in the map.
- First posted on arXiv 10 Jul 2023.

## Builds on (most cited first, 8 of 8)

- [A megapixel time-gated SPAD image sensor for 2D and 3D imaging applications](https://mlsanity.com/paper/1912.12910/) (2019)
- [Asynchronous Single-Photon 3D Imaging](https://mlsanity.com/paper/1908.06372/) (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)
- [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)
- [A direct time-of-flight image sensor with in-pixel surface detection and dynamic vision](https://mlsanity.com/paper/2209.11772/) (2022)
- [Approximate Queries and Representations for Large Data Sequences](https://mlsanity.com/paper/1904.09262/) (2019)

## Built on by (most cited first, 4 of 4)

- [Single-Photon 3D Imaging with Equi-Depth Photon Histograms](https://mlsanity.com/paper/2408.16150/) (2024)
- [Hardware-aware Coding Function Design for Compressive Single-Photon 3D Cameras](https://mlsanity.com/paper/2510.12123/) (2025)
- [Ghosts in the Point Clouds: De-glaring LiDAR in the Transient Domain](https://mlsanity.com/paper/2605.24753/) (2026)
- [High-Flux Count-Free Single-Photon 3D Cameras](https://mlsanity.com/paper/2608.18306/) (2026)

## Abstract (arXiv)

Single-photon cameras (SPCs) have emerged as a promising technology for high-resolution 3D imaging. A single-photon 3D camera determines the round-trip time of a laser pulse by capturing the arrival of individual photons at each camera pixel. Constructing photon-timestamp histograms is a fundamental operation for a single-photon 3D camera. However, in-pixel histogram processing is computationally expensive and requires large amount of memory per pixel. Digitizing and transferring photon timestamps to an off-sensor histogramming module is bandwidth and power hungry. Here we present an online approach for distance estimation without explicitly storing photon counts. The two key ingredients of our approach are (a) processing photon streams using race logic, which maintains photon data in the time-delay domain, and (b) constructing count-free equi-depth histograms. Equi-depth histograms are a succinct representation for ``peaky'' distributions, such as those obtained by an SPC pixel from a laser pulse reflected by a surface. Our approach uses a binner element that converges on the median (or, more generally, to another quantile) of a distribution. We cascade multiple binners to form an equi-depth histogrammer that produces multi-bin histograms. Our evaluation shows that this method can provide an order of magnitude reduction in bandwidth and power consumption while maintaining similar distance reconstruction accuracy as conventional processing methods.