arXiv subjects

Yihao Yu

Publications and source records attributed to Yihao Yu.

4 recordsLinked to original sources

arXiv2026

Collective Blinking of Upconversion Emission in Lanthanide-doped Nanocrystals

Fluorescence blinking, often regarded as a limitation for stable emitters, can enable super-resolution localization microscopy and serve as a versatile reporter of the photophysical states of quantum emitters and their interactions with local environment. However, conventional blinking emitters are typically single quantum systems with Stokes-shifted fluorescence, making them susceptible to autofluorescence background, weak signal, and irreversible photodegradation under prolonged excitation. In contrast, single lanthanide-doped upconversion nanocrystals are effectively background-free anti-Stokes emitters and demonstrate robust resistance to photodegradation, yet they are generally considered non-blinking owing to the presence of a large ensemble of uncorrelated emitting lanthanide ions within a single nanocrystal. Here we report the discovery and control of collective blinking in the upconversion luminescence of thousands of lanthanide ions within a single nanocrystal. The blinking exhibits on-off intensity ratio exceeding 10, persists for over 15 hours (over 10,000 cycles) without discernible photodegradation, and can be reversibly controlled by adjusting the excitation power. We elucidate a universal, activator-independent upconversion blinking mechanism, whereby a single quencher, stochastically generated via a cooperative multi-ion process, can intercept delocalized excitation energy within the Yb3+ sensitizer network and darken the whole nanocrystal. Benefiting from the high-contrast, long-term photostable blinking and background-free emission, we achieve robust super-resolution localization microscopy that resolves individual nanocrystals in aggregates with 1.2 nm precision. This work establishes a general strategy to realize and control collective blinking in photostable multi-emitter nanosystems, opening new opportunities in nanoscience, bioimaging, and quantum technologies.

physics.optics↗

arXiv2025

Photothermal Fourier-plane Phase Synchronization for Interferometric Scattering Microscopy

We introduce and experimentally implement Fourier-plane phase synchronization for optical microscopy, and demonstrate its performance with interferometric scattering microscopy. By combining a photothermal phase plate and laser beam scanning, we realize a synchronized phase for all scattering components on the Fourier plane of high numerical-aperture microscopes, where the evanescent waves and optical aberration normally produce highly inhomogeneous phase distributions. We achieve an almost perfect point spread function, exhibiting a tighter focus with 50\% enhancement of the signal and ideal circular symmetry. Particularly, by synchronizing the phase to $\pi/2$, we demonstrate the background speckles exhibit an anti-symmetric dependence on axial defocus, enabling the effective suppression of the speckles via defocus integration and thus the detection of 10 nm particles immobilized on the substrate. The concept and technique of seamless dynamic phase control on the Fourier plane constitute a key asset for modern optical microscopy.

physics.optics↗

arXiv2024

LiVisSfM: Accurate and Robust Structure-from-Motion with LiDAR and Visual Cues

This paper presents an accurate and robust Structure-from-Motion (SfM) pipeline named LiVisSfM, which is an SfM-based reconstruction system that fully combines LiDAR and visual cues. Unlike most existing LiDAR-inertial odometry (LIO) and LiDAR-inertial-visual odometry (LIVO) methods relying heavily on LiDAR registration coupled with Inertial Measurement Unit (IMU), we propose a LiDAR-visual SfM method which innovatively carries out LiDAR frame registration to LiDAR voxel map in a Point-to-Gaussian residual metrics, combined with a LiDAR-visual BA and explicit loop closure in a bundle optimization way to achieve accurate and robust LiDAR pose estimation without dependence on IMU incorporation. Besides, we propose an incremental voxel updating strategy for efficient voxel map updating during the process of LiDAR frame registration and LiDAR-visual BA optimization. Experiments demonstrate the superior effectiveness of our LiVisSfM framework over state-of-the-art LIO and LIVO works on more accurate and robust LiDAR pose recovery and dense point cloud reconstruction of both public KITTI benchmark and a variety of self-captured dataset.

cs.CV↗

arXiv2023

Depth Completion with Multiple Balanced Bases and Confidence for Dense Monocular SLAM

Dense SLAM based on monocular cameras does indeed have immense application value in the field of AR/VR, especially when it is performed on a mobile device. In this paper, we propose a novel method that integrates a light-weight depth completion network into a sparse SLAM system using a multi-basis depth representation, so that dense mapping can be performed online even on a mobile phone. Specifically, we present a specifically optimized multi-basis depth completion network, called BBC-Net, tailored to the characteristics of traditional sparse SLAM systems. BBC-Net can predict multiple balanced bases and a confidence map from a monocular image with sparse points generated by off-the-shelf keypoint-based SLAM systems. The final depth is a linear combination of predicted depth bases that can be optimized by tuning the corresponding weights. To seamlessly incorporate the weights into traditional SLAM optimization and ensure efficiency and robustness, we design a set of depth weight factors, which makes our network a versatile plug-in module, facilitating easy integration into various existing sparse SLAM systems and significantly enhancing global depth consistency through bundle adjustment. To verify the portability of our method, we integrate BBC-Net into two representative SLAM systems. The experimental results on various datasets show that the proposed method achieves better performance in monocular dense mapping than the state-of-the-art methods. We provide an online demo running on a mobile phone, which verifies the efficiency and mapping quality of the proposed method in real-world scenarios.

cs.CV↗