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Dynamic wavefront shaping with optical metasurfaces has presented a major challenge and inspired a large number of highly elaborate solutions. Here, we experimentally demonstrate thermo-optically reconfigurable, nonlocal metasurfaces using…
Polynorbornene-based decomposable polymer which can be patterned with ultraviolet or electron-beam radiation is used to create micrometer-scale fluidic channels. Silicon nitride substrates are used to fabricate nanophotonic wavegide and…
We propose a learning-based method that solves monocular stereo and can be extended to fuse depth information from multiple target frames. Given two unconstrained images from a monocular camera with known intrinsic calibration, our network…
Depth estimation from a single image represents a fascinating, yet challenging problem with countless applications. Recent works proved that this task could be learned without direct supervision from ground truth labels leveraging image…
The photonic lantern (PL) is a tapered waveguide that can efficiently couple light into multiple single-mode optical fibers. Such devices are currently being considered for a number of tasks, including the coupling of telescopes and…
Deep learning techniques have enabled rapid progress in monocular depth estimation, but their quality is limited by the ill-posed nature of the problem and the scarcity of high quality datasets. We estimate depth from a single camera by…
We aim to generate high resolution shallow depth-of-field (DoF) images from a single all-in-focus image with controllable focal distance and aperture size. To achieve this, we propose a novel neural network model comprised of a depth…
This paper reports an experimental demonstration of moir\'e metalens which shows wide focal length tunability from negative to positive by mutual angle rotation at the wavelength of 900 nm. The moir\'e metalens was developed using high…
In recent years, the emergence of foundation models for depth prediction has led to remarkable progress, particularly in zero-shot monocular depth estimation. These models generate impressive depth predictions; however, their outputs are…
Live cell imaging using metallic nanoparticles as tags is an emerging technique to visualize long and highly dynamic processes due to the lack of photobleaching and high photon rate. However, the lack of excited states as compared to…
Dispersion engineering is essential to the performance of most modern optical systems including fiber-optic devices. Even though the chromatic dispersion of a meter-scale single-mode fiber used for endoscopic applications is negligible,…
Advances in the field of plasmonics, that is, nanophotonics based on optical properties of metal nanostructures, paved the way for the development of ultrasensitive biological sensors and other devices whose operating principles are based…
Accurate distance estimation is a fundamental challenge in robotic perception, particularly in omnidirectional imaging, where traditional geometric methods struggle with lens distortions and environmental variability. In this work, we…
We introduce the resonant metalens, a cluster of coupled subwavelength resonators. Dispersion allows the conversion of subwavelength wavefields into temporal signatures while the Purcell effect permits an efficient radiation of this…
Metalenses built up by artificial sub-wavelength nanostructures have shown the capability of realizing light focusing with miniature lens size. To date, most of the reported metalenses were patterned using electron beam lithography (EBL),…
Multi-frame depth estimation improves over single-frame approaches by also leveraging geometric relationships between images via feature matching, in addition to learning appearance-based features. In this paper we revisit feature matching…
Metal nanostructures act as powerful optical antennas[1, 2] because collective modes of the electron fluid in the metal are excited when light strikes the surface of the nanostructure. These excitations, known as plasmons, can have…
Deterministic optical manipulation of fluorescent nanodiamonds (FNDs) in fluids has emerged as an experimental challenge in multimodal biological imaging. Designing and developing nano-optical trapping strategies to serve this purpose is an…
Optical neural networks (ONNs) are gaining increasing attention to accelerate machine learning tasks. In particular, static meta-optical encoders designed for task-specific pre-processing have demonstrated orders of magnitude smaller energy…
We introduce a novel framework for metric depth estimation that enhances pretrained diffusion-based monocular depth estimation (DB-MDE) models with stereo vision guidance. While existing DB-MDE methods excel at predicting relative depth,…