Related papers: Accurate Simulation Pipeline for Passive Single-Ph…
We propose a method for depth estimation under different illumination conditions, i.e., day and night time. As photometry is uninformative in regions under low-illumination, we tackle the problem through a multi-sensor fusion approach,…
Few-shot object detection (FSOD) aims to expand an object detector for novel categories given only a few instances for training. The few training samples restrict the performance of FSOD model. Recent text-to-image generation models have…
Integrated photodetectors are essential components of scalable photonics platforms for quantum and classical applications. However, most efforts in the development of such devices to date have been focused on infrared telecommunications…
Recently, proposal-free instance segmentation has received increasing attention due to its concise and efficient pipeline. Generally, proposal-free methods generate instance-agnostic semantic segmentation labels and instance-aware features…
Training autonomous vehicles requires lots of driving sequences in all situations\cite{zhao2016}. Typically a simulation environment (software-in-the-loop, SiL) accompanies real-world test drives to systematically vary environmental…
Achieving fast, sensitive, and parallel measurement of a large number of quantum particles is an essential task in building large-scale quantum platforms for different quantum information processing applications such as sensing,…
Illuminant estimation plays a key role in digital camera pipeline system, it aims at reducing color casting effect due to the influence of non-white illuminant. Recent researches handle this task by using Convolution Neural Network (CNN) as…
Automated classification of supernovae (SNe) based on optical photometric light curve information is essential in the upcoming era of wide-field time domain surveys, such as the Legacy Survey of Space and Time (LSST) conducted by the Rubin…
Superconducting nanowire single-photon detectors (SNSPDs) are the current leading technology for the detection of single-photons in the near-infrared (NIR) and short-wave infrared (SWIR) spectral regions, due to record performance in terms…
Single photon detectors have dark count rates that depend strongly on the bias level for detector operation. In the case of weak light sources such as novel lasers or single-photon emitters, the rate of counts due to the light source can be…
Current count-rate models for single-photon avalanche diodes (SPADs) typically assume an instantaneous recovery of the quantum efficiency following dead-time, leading to a systematic overestimation of the effective detection efficiency for…
Diffusion models have demonstrated impressive performance in various image generation, editing, enhancement and translation tasks. In particular, the pre-trained text-to-image stable diffusion models provide a potential solution to the…
Single photon detection is the underpinning technology for quantum communication and quantum sensing applications. At visible and near-infrared wavelengths, single-photon-detectors (SPDs) underwent a significant development in the past two…
InGaAs/InP single-photon avalanche diodes (SPADs) working in the regime of GHz clock rates are crucial components for the high-speed quantum key distribution (QKD). We have developed for the first time a compact, stable and user-friendly…
The effective and convenient detection of single photons via advanced detectors with a large active area is becoming significant for quantum and classical applications. This work demonstrates the fabrication of a superconducting microstrip…
Travelling modes of single-photon-added coherent states (SPACS) are characterized via optical homodyne tomography. Given a set of experimentally measured quadrature distributions, we estimate parameters of the state and also extract…
While single-photon counting is routinely achieved in the optical domain, operational single microwave photon detectors (SMPDs) have only recently been demonstrated. SMPDs are critical for sensing weak signals from incoherent emitters, with…
Estimating scene geometry from data obtained with cost-effective sensors is key for robots and self-driving cars. In this paper, we study the problem of predicting dense depth from a single RGB image (monodepth) with optional sparse…
Monocular depth estimation is a crucial task in computer vision. While existing methods have shown impressive results under standard conditions, they often face challenges in reliably performing in scenarios such as low-light or rainy…
Phase-sensitive detection (PSD) is an important experimental technique that allows signals to be extracted from noisy data. PSD is also used in modulation spectroscopy and is used in the stabilization of optical sources. Commercial lock-in…