Video super-resolution for single-photon LIDAR

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 th...

Full description

Saved in:
Bibliographic Details
Published in:arXiv.org 2022-10
Main Authors: Germán Mora Martín, Scholes, Stirling, Ruget, Alice, Henderson, Robert K, Leach, Jonathan, Gyongy, Istvan
Format: Article
Language:English
Subjects:
Artificial neural networks
Augmented reality
Autonomous cars
Avalanche diodes
Frames (data processing)
Frames per second
Obstacle avoidance
Photon avalanches
Photons
Robotics
Sensor arrays
Sensors
Signal to noise ratio
Online Access:Get full text
Tags: Add Tag
No Tags, Be the first to tag this record!
Description
Summary: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.
ISSN:2331-8422
DOI:10.48550/arxiv.2210.10474