Search

3D Time-of-Flight (ToF) image sensors are used widely in applications such as self-driving cars, Augmented Reality (AR) and robotics. When implemented with Single-Photon Avalanche Diodes (SPADs), compact, array format sensors can be made that offer accurate depth maps over long distances, without the need for mechanical scanning. However, array sizes tend to be small, leading to low lateral resolution, which combined with low Signal-to-Noise Ratio (SNR) levels under high ambient illumination, may lead to difficulties in scene interpretation. In this paper, we use synthetic depth sequences to train a 3D Convolutional Neural Network (CNN) for denoising and upscaling (x4) depth data. Experimental results, based on synthetic as well as real ToF data, are used to demonstrate the effectiveness of the scheme. With GPU acceleration, frames are processed at >30 frames per second, making the approach suitable for low-latency imaging, as required for obstacle avoidance., Comment: 18 pages, 10 figures, 3 tables

Single-Photon Avalanche Detector (SPAD) arrays are a rapidly emerging technology. These multi-pixel sensors have single-photon sensitivities and pico-second temporal resolutions thus they can rapidly generate depth images with millimeter precision. Such sensors are a key enabling technology for future autonomous systems as they provide guidance and situational awareness. However, to fully exploit the capabilities of SPAD array sensors, it is crucial to establish the quality of depth images they are able to generate in a wide range of scenarios. Given a particular optical system and a finite image acquisition time, what is the best-case depth resolution and what are realistic images generated by SPAD arrays? In this work, we establish a robust yet simple numerical procedure that rapidly establishes the fundamental limits to depth imaging with SPAD arrays under real world conditions. Our approach accurately generates realistic depth images in a wide range of scenarios, allowing the performance of an optical depth imaging system to be established without the need for costly and laborious field testing. This procedure has applications in object detection and tracking for autonomous systems and could be easily extended to systems for underwater imaging or for imaging around corners.

The growing ubiquity of drones has raised concerns over the ability of traditional air-space monitoring technologies to accurately characterise such vehicles. Here, we present a CNN using a decision tree and ensemble structure to fully characterise drones in flight. Our system determines the drone type, orientation (in terms of pitch, roll, and yaw), and performs segmentation to classify different body parts (engines, body, and camera). We also provide a computer model for the rapid generation of large quantities of accurately labelled photo-realistic training data and demonstrate that this data is of sufficient fidelity to allow the system to accurately characterise real drones in flight. Our network will provide a valuable tool in the image processing chain where it may build upon existing drone detection technologies to provide complete drone characterisation over wide areas.

The process of tracking human anatomy in computer vision is referred to pose estimation, and it is used in fields ranging from gaming to surveillance. Three-dimensional pose estimation traditionally requires advanced equipment, such as multiple linked intensity cameras or high-resolution time-of-flight cameras to produce depth images. However, there are applications, e.g.~consumer electronics, where significant constraints are placed on the size, power consumption, weight and cost of the usable technology. Here, we demonstrate that computational imaging methods can achieve accurate pose estimation and overcome the apparent limitations of time-of-flight sensors designed for much simpler tasks. The sensor we use is already widely integrated in consumer-grade mobile devices, and despite its low spatial resolution, only 4$\times$4 pixels, our proposed Pixels2Pose system transforms its data into accurate depth maps and 3D pose data of multiple people up to a distance of 3 m from the sensor. We are able to generate depth maps at a resolution of 32$\times$32 and 3D localization of a body parts with an error of only $\approx$10 cm at a frame rate of 7 fps. This work opens up promising real-life applications in scenarios that were previously restricted by the advanced hardware requirements and cost of time-of-flight technology.

Laser brightness is crucial in many optical processes, and is optimised by high power, high beam quality (low $M^2$) beams. Here we show how to improve the laser beam quality factor (reducing the $M^2$) of arbitrary structured light fields in a lossless manner using continuous phase-only elements, thus allowing for the increase in brightness by a simple linear optical transformation. We demonstrate the principle with four high $M^2$ initial beams, converting each to a Gaussian ($M^2 \approx 1$) with a dramatic increase in brightness of $>10 \times$. This work puts a new perspective on the old debate of improving laser beam quality with binary diffractive optics, while providing a practical approach to enhancing laser brightness for arbitrary input beams.

Structured light concerns the control of light in its spatial degrees of freedom (amplitude, phase and polarization), and has proven instrumental in many applications. The creation of structured light usually involves the conversion of a Gaussian mode to a desired structure in a single step, while the detection is often the reverse process, both fundamentally lossy or imperfect. Here we show how to ideally reshape structured light in a lossless manner in a simple two-step process. We outline the core theoretical arguments, and demonstrate reshaping of arbitrary structured light patterns, in the process highlighting when the technique is applicable and when not, and how best to implement it. This work will be a useful addition to the structured light toolkit, and particularly relevant to those wishing to use the spatial modes of light as a basis in classical and quantum communication.

Quantum communication over long distances is integral to information security and has been demonstrated in free space and fibre with two-dimensional polarisation states of light. Although increased bit rates can be achieved using high-dimensional encoding with spatial modes of light, the efficient detection of high-dimensional states remains a challenge to realise the full benefit of the increased state space. Here we exploit the entanglement between spatial modes and polarization to realise a four-dimensional quantum key distribution (QKD) protocol. We introduce a detection scheme which employs only static elements, allowing for the detection of all basis modes in a high-dimensional space deterministically. As a result we are able to realise the full potential of our high-dimensional state space, demonstrating efficient QKD at high secure key and sift rates, with the highest capacity-to-dimension reported to date. This work opens the possibility to increase the dimensionality of the state-space indefinitely while still maintaining deterministic detection and will be invaluable for long distance 'secure and fas' data transfer., Comment: 9 pages, version 1