Lab Group

Welcome to Computer Vision & Spatial AI Lab

Advancing the frontiers of vision computing and artificial intelligence

Explore Our Research

Our Research Areas

3D Visual-Spatial Perception

Learning-based Visual Localization

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

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

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

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

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

Hand Gesture Recognition

Developing hand gesture recognition systems with HoloLens 2-based mixed reality technology for intuitive human-computer interaction.

Lab News

Nov 8, 20251 paper accepted at AAAI 2026. Congratulations to Sun Jo and Ahjin Choi!#Publication
Sep 13, 20251 paper accepted at Engineering Applications of Artificial Intelligence (IF 8.0, Categorical JCR < 2.6%). Congratulations to Taekyeong Park, Yongho Son, and Sanghyuk Moon!#Publication
Sep 1, 2025Our lab won a grant from the National Research Foundation of Korea (NRF)!#News
Aug 27, 2025Je Hyeong Hong is serving as an Area for 3DV 2026!#News
Jul 21, 2025Heejoon Moon accepted a summer internship offer from Hyundai Rotem. Congratulations!#News

Recent Publications

PINGS-X: Physics-Informed Normalized Gaussian Splatting with Axes Alignment for Efficient Super-Resolution of 4D Flow MRI

Sun Jo*, Seok Young Hong*, JinHyun Kim, Seungmin Kang, Ahjin Choi, Don-Gwan An, Simon Song† and Je Hyeong Hong†

Association for the Advancement of Artificial Intelligence, AAAI 2026

Long-tailed Detection and Classification of Wafer Defects from Scanning Electron Micro-scope Images Robust to Diverse Image Backgrounds and Defect Scales

Taekyeong Park*, Yongho Son*, Sanghyuk Moon*, Seungju Han and Je Hyeong Hong

Engineering Applications of Artificial Intelligence (EAAI, IF 8.0, Categorical JCR < 2.6%), Collaboration work with Samsung AI Center

GAMT: A Geometry-Aware, Multi-view, Training-free Segmentation Framework for Foundation Models in Medical Imaging

Sun Jo*, Ahjin Choi* and Je Hyeong Hong

IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshop (CVPRw) 2025

Join Our Lab

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

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