# Hardware-aware Coding Function Design for Compressive Single-Photon 3D Cameras

arXiv: https://arxiv.org/abs/2510.12123 · HTML: https://mlsanity.com/paper/2510.12123/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 15 of its 71 references that are in the map.
- First posted on arXiv 14 Oct 2025.

## Builds on (most cited first, 15 of 15)

- [Adam: A Method for Stochastic Optimization](https://mlsanity.com/paper/1412.6980/) (2014)
- [Virtual Wave Optics for Non-Line-of-Sight Imaging](https://mlsanity.com/paper/1810.07535/) (2018)
- [DELTAR: Depth Estimation from a Light-weight ToF Sensor and RGB Image](https://mlsanity.com/paper/2209.13362/) (2022)
- [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)
- [Towards 3D Vision with Low-Cost Single-Photon Cameras](https://mlsanity.com/paper/2403.17801/) (2024)
- [Real-time 3D reconstruction from single-photon lidar data using plug-and-play point cloud denoisers](https://mlsanity.com/paper/1905.06700/) (2019)
- [SoDaCam: Software-defined Cameras via Single-Photon Imaging](https://mlsanity.com/paper/2309.00066/) (2023)
- [Generalized Event Cameras](https://mlsanity.com/paper/2407.02683/) (2024)
- [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)
- [Video super-resolution for single-photon LIDAR](https://mlsanity.com/paper/2210.10474/) (2022)
- [Single-Photon 3D Imaging with Equi-Depth Photon Histograms](https://mlsanity.com/paper/2408.16150/) (2024)
- [Surface Detection for Sketched Single Photon Lidar](https://mlsanity.com/paper/2105.06920/) (2021)
- [FoveaSPAD: Exploiting Depth Priors for Adaptive and Efficient Single-Photon 3D Imaging](https://mlsanity.com/paper/2412.02052/) (2024)

## Abstract (arXiv)

Single-photon cameras are becoming increasingly popular in time-of-flight 3D imaging because they can time-tag individual photons with extreme resolution. However, their performance is susceptible to hardware limitations, such as system bandwidth, maximum laser power, sensor data rates, and in-sensor memory and compute resources. Compressive histograms were recently introduced as a solution to the challenge of data rates through an online in-sensor compression of photon timestamp data. Although compressive histograms work within limited in-sensor memory and computational resources, they underperform when subjected to real-world illumination hardware constraints. To address this, we present a constrained optimization approach for designing practical coding functions for compressive single-photon 3D imaging. Using gradient descent, we jointly optimize an illumination and coding matrix (i.e., the coding functions) that adheres to hardware constraints. We show through extensive simulations that our coding functions consistently outperform traditional coding designs under both bandwidth and peak power constraints. This advantage is particularly pronounced in systems constrained by peak power. Finally, we show that our approach adapts to arbitrary parameterized impulse responses by evaluating it on a real-world system with a non-ideal impulse response function.