Free-running vs. Synchronous: Single-Photon Lidar for High-flux 3D Imaging

TL;DR

This paper compares free-running and synchronous single-photon lidar systems, proposing a new estimator and regularization method that improve depth and flux estimation accuracy, especially in free-running systems.

Contribution

It introduces a computationally efficient joint maximum likelihood estimator and a regularization framework with a learned prior for free-running SPL.

Findings

Free-running SPL outperforms synchronous SPL in estimation accuracy.

The proposed methods reduce errors in depth and flux estimation.

Regularization with a learned prior enhances accuracy further.

Abstract

Conventional wisdom suggests that single-photon lidar (SPL) should operate in low-light conditions to minimize dead-time effects. Many methods have been developed to mitigate these effects in synchronous SPL systems. However, solutions for free-running SPL remain limited despite the advantage of reduced histogram distortion from dead times. To improve the accuracy of free-running SPL, we propose a computationally efficient joint maximum likelihood estimator of the signal flux, the background flux, and the depth using only histograms, along with a complementary regularization framework that incorporates a learned point cloud score model as a prior. Simulations and experiments demonstrate that free-running SPL yields lower estimation errors than its synchronous counterpart under identical conditions, with our regularization further improving accuracy.