Photon-Efficient Computational 3D and Reflectivity Imaging with Single-Photon Detectors

TL;DR

This paper introduces a computational imaging method that accurately captures 3D depth and reflectivity images using only about one photon per pixel, significantly improving photon efficiency over traditional methods.

Contribution

The authors develop a robust, physically accurate computational framework that estimates depth and reflectivity from extremely low photon counts, outperforming conventional maximum-likelihood approaches.

Findings

Achieves accurate depth and reflectivity imaging with approximately one photon per pixel.

Increases photon efficiency by 100 times compared to traditional methods.

Demonstrates robustness under strong background light conditions.

Abstract

Capturing depth and reflectivity images at low light levels from active illumination of a scene has wide-ranging applications. Conventionally, even with single-photon detectors, hundreds of photon detections are needed at each pixel to mitigate Poisson noise. We develop a robust method for estimating depth and reflectivity using on the order of 1 detected photon per pixel averaged over the scene. Our computational imager combines physically accurate single-photon counting statistics with exploitation of the spatial correlations present in real-world reflectivity and 3D structure. Experiments conducted in the presence of strong background light demonstrate that our computational imager is able to accurately recover scene depth and reflectivity, while traditional maximum-likelihood based imaging methods lead to estimates that are highly noisy. Our framework increases photon efficiency 100-fold over traditional processing and also improves, somewhat, upon first-photon imaging under a total acquisition time constraint in raster-scanned operation. Thus our new imager will be useful for rapid, low-power, and noise-tolerant active optical imaging, and its fixed dwell time will facilitate parallelization through use of a detector array.