A5103113428- Infrared Target Detection Methodologies
- Advanced Neural Network Applications
- Advanced Optical Sensing Technologies
- CCD and CMOS Imaging Sensors
- Advanced Vision and Imaging
- Image Enhancement Techniques
- Robotics and Sensor-Based Localization
- Computer Graphics and Visualization Techniques
- Artificial Intelligence in Healthcare and Education
- Advanced Image and Video Retrieval Techniques
- Industrial Vision Systems and Defect Detection
- Optical measurement and interference techniques
- Surgical Simulation and Training
- Domain Adaptation and Few-Shot Learning
- Indoor and Outdoor Localization Technologies
- Simulation-Based Education in Healthcare
- Multimodal Machine Learning Applications
- Advanced Image Fusion Techniques
- Hydrocarbon exploration and reservoir analysis
- Robotic Path Planning Algorithms
Ningbo University
2023-2025
UNSW Sydney
2025
East China University of Science and Technology
2024
Abstract Neural rendering techniques, such as Radiance Fields (NeRF) and 3D Gaussian Splatting (3D‐GS), have led to significant advancements in scene reconstruction novel view synthesis (NVS). These methods assume the use of an ideal pinhole model, which is free from lens distortion optical aberrations. However, fisheye lenses introduce unavoidable aberrations due their wide‐angle design complex manufacturing, leading multi‐view inconsistencies that compromise quality. In this paper, we...
<title>Abstract</title> Background Artificial Intelligence(AI) is advancing, but its role in simulating detailed patient-doctor interactions the style of Objective Structured Clinical Examinations(OSCEs) emerging. This study's goal was to create and validate an AI virtual patient(AIVP) that could interact with medical students, mimic a patient issue, provide students feedback on their performance. Methods Six AIVP were developed simulate OSCE scenarios for common emergency department...
We propose Multi-spectral Neural Radiance Fields(Spec-NeRF) for jointly reconstructing a multispectral radiance field and spectral sensitivity functions(SSFs) of the camera from set color images filtered by different filters. The proposed method focuses on modeling physical imaging process, applies estimated SSFs to synthesize novel views scenes. In this method, data acquisition requires only low-cost trichromatic several off-the-shelf filters, making it more practical than using specialized...
<p>Spec-NeRF jointly optimizes the degradation parameters and achieves high-quality multi-spectral image reconstruction results at novel views, which only requires a low-cost camera (like phone but in RAW mode) several off-the-shelf color filters. We also provide real scenarios synthetic datasets for related studies. Code is available <a href="https://github.com/CPREgroup/SpecNeRF-v2" target="_blank">https://github.com/CPREgroup/SpecNeRF-v2</a></p>
<p>Spec-NeRF jointly optimizes the degradation parameters and achieves high-quality multi-spectral image reconstruction results at novel views, which only requires a low-cost camera (like phone but in RAW mode) several off-the-shelf color filters. We also provide real scenarios synthetic datasets for related studies. Code is available <a href="https://github.com/CPREgroup/SpecNeRF-v2" target="_blank">https://github.com/CPREgroup/SpecNeRF-v2</a></p>
We propose Multi-spectral Neural Radiance Fields(Spec-NeRF) for jointly reconstructing a multispectral radiance field and spectral sensitivity functions(SSFs) of the camera from set color images filtered by different filters. The proposed method focuses on modeling physical imaging process, applies estimated SSFs to synthesize novel views scenes. In this method, data acquisition requires only low-cost trichromatic several off-the-shelf filters, making it more practical than using specialized...