Welcome to Computer Vision & Spatial AI Lab
Advancing the frontiers of vision computing and artificial intelligence
Explore Our ResearchFeatured Research Openings
We are looking for curious and self-motivated students who want to pursue impactful research in computer vision and spatial AI.
3D Segmentation
We are seeking motivated students interested in robust 3D understanding for medical, spatial, and real-world scene analysis.
Topics include volumetric segmentation, structure-aware representation learning, cross-view fusion, and 3D reconstruction pipelines.
Ideal for students with experience in computer vision, medical imaging, geometry, or deep learning.
Physics-Informed Learning
We welcome applicants who want to build learning frameworks that respect physical constraints and improve interpretability.
Current interests include physics-informed neural networks, simulation-aware learning, spatio-temporal modeling, and scientific AI for imaging data.
A strong fit for students who enjoy machine learning, applied mathematics, inverse problems, or computational imaging.
Hyperspectral Image Processing
We are recruiting students to work on multimodal representation learning and practical vision-language systems for hyperspectral imagery.
Research directions include spectral-spatial modeling, multimodal foundation models, domain adaptation, and industrial deployment.
Relevant backgrounds include remote sensing, multimodal learning, pattern recognition, and efficient model design.
Our Research Areas
3D Visual-Spatial Perception
Learning-based Visual Localization
Our research centers on learning-based visual localization, which aims to infer precise camera poses using deep learning techniques. Among various approaches, we explore Scene Coordinate Regression (SCR)—a method that predicts dense or sparse 3D scene points directly from sensor data like 2D images or LiDAR scans. This enables robust localization by linking 2D observations with the 3D world geometry.
Privacy-Preserving Visual Localization
Developing techniques that enable visual localization while protecting privacy concerns through methods like paired-point lifting and 3D ray clouds.
3D Object Reassembly and Reconstruction
Reconstructing the original shape of heritage ceramics from multiple fragments using advanced 3D modeling and matching techniques.
Multimodal AI for Industrial Anomaly Detection
Generative AI for Anomaly Image Generation
Development of a Few Shot-based AI Algorithm for Augmentation and Generation of Defect Data.
Vision-Based Defect Detection
Developing algorithms for detecting industrial defects and generating synthetic defect images for training robust models.
Video Anomaly Detection
Detecting anomalous human behaviors in video surveillance through unsupervised and semi-supervised learning approaches.
Medical-Spatial AI
Physics-Informed AI
Using axes-aligned spatiotemporal Gaussians and a physics-informed normalized splatting scheme to efficiently super-resolve 4D flow MRI data.
Medical Image Segmentation and 3D Reconstruction
CT-based carotid artery segmentation and 3D vessel structure reconstruction for improved medical diagnosis and treatment planning.
Hand Gesture Recognition
Developing hand gesture recognition systems with HoloLens 2-based mixed reality technology for intuitive human-computer interaction.
Lab News
Recent Publications
Revisiting Geometric Obfuscation with Dual Convergent Lines for Privacy-Preserving Image Queries in Visual Localization
Jeong Gon Kim, Heejoon Moon and Je Hyeong Hong
IEEE/CVF Conference on Computer Vision and Pattern Recognition, CVPR 2026
Shape-of-You: Fused Gromov-Wasserstein Optimal Transport for Semantic Correspondence in-the-Wild
Jiin Im, Sisung Liu and Je Hyeong Hong
IEEE/CVF Conference on Computer Vision and Pattern Recognition, CVPR 2026
Join Our Lab
MS/PhD Recruitment
We are actively recruiting students who are excited to work on 3D segmentation, physics-informed learning, and hyperspectral image processing.
Applicants with backgrounds in computer vision, machine learning, medical imaging, remote sensing, robotics, or related areas are especially encouraged to apply.
Current focus
- 3D segmentation and reconstruction
- Physics-informed and simulation-aware learning
- Hyperspectral multimodal understanding
Contact Us
Location:
Engineering Center Annex Unit 415-1
Hanyang University
222 Wangsimni-ro
Seongdong-gu
Seoul, 04763
Republic of Korea
Email:
jhh37 at hanyang dot ac dot kr
Telephone:
02-2220-2489