Spatial images from temporal data

TL;DR

This paper introduces a data-driven method to generate 3D images using only a single-point sensor and temporal data, bypassing traditional spatial imaging requirements, enabling faster and more compact imaging systems.

Contribution

It demonstrates a novel approach to 3D imaging using single-point time-of-flight data and deep learning, removing the need for spatial structures in detectors or illumination.

Findings

Achieved 3D imaging with a single-photon detector and temporal data.

Enabled compact RF radar to produce 3D images using this method.

Showed potential for faster, smaller imaging systems.

Abstract

Traditional paradigms for imaging rely on the use of a spatial structure, either in the detector (pixels arrays) or in the illumination (patterned light). Removal of the spatial structure in the detector or illumination, i.e., imaging with just a single-point sensor, would require solving a very strongly ill-posed inverse retrieval problem that to date has not been solved. Here, we demonstrate a data-driven approach in which full 3D information is obtained with just a single-point, single-photon avalanche diode that records the arrival time of photons reflected from a scene that is illuminated with short pulses of light. Imaging with single-point time-of-flight (temporal) data opens new routes in terms of speed, size, and functionality. As an example, we show how the training based on an optical time-of-flight camera enables a compact radio-frequency impulse radio detection and ranging transceiver to provide 3D images.