Single-Photon Depth Imaging Using a Union-of-Subspaces Model

TL;DR

This paper presents a new method for accurate depth imaging with single-photon detectors that works effectively with very few photons and unknown background light, outperforming traditional methods.

Contribution

It introduces a novel union-of-subspaces model combined with a greedy algorithm for rapid, accurate depth and background flux estimation from minimal photon data.

Findings

Achieves 1.7 cm depth accuracy with only 15 photons per pixel.

Outperforms conventional log-matched filtering by a factor of 6.1 in depth error.

Works without assumptions on spatial correlation of depth or background flux.

Abstract

Light detection and ranging systems reconstruct scene depth from time-of-flight measurements. For low light-level depth imaging applications, such as remote sensing and robot vision, these systems use single-photon detectors that resolve individual photon arrivals. Even so, they must detect a large number of photons to mitigate Poisson shot noise and reject anomalous photon detections from background light. We introduce a novel framework for accurate depth imaging using a small number of detected photons in the presence of an unknown amount of background light that may vary spatially. It employs a Poisson observation model for the photon detections plus a union-of-subspaces constraint on the discrete-time flux from the scene at any single pixel. Together, they enable a greedy signal-pursuit algorithm to rapidly and simultaneously converge on accurate estimates of scene depth and background flux, without any assumptions on spatial correlations of the depth or background flux. Using experimental single-photon data, we demonstrate that our proposed framework recovers depth features with 1.7 cm absolute error, using 15 photons per image pixel and an illumination pulse with 6.7-cm scaled root-mean-square length. We also show that our framework outperforms the conventional pixelwise log-matched filtering, which is a computationally-efficient approximation to the maximum-likelihood solution, by a factor of 6.1 in absolute depth error.