전진우

Generating free-viewpoint 3D 360° VR content from multi-camera video so users can move and interact in a virtual space.

A WISE (Wearable Interface for Sustainable Enhancement) AR UI/UX platform for smart glasses.

불완전한 기억의 공간 가시화 재구성 기술 개발

This paper presents an augmented reality (AR) authoring system that enables an ordinary user to easily build an AR environment by manipulating and placing 3D virtual objects. The system tracks users’ hand motions via an RGB-D camera which built-in an optical see-through (OST) head mounted display (HMD), and interactive features applied to virtual objects by the pre-defined hand gestures. In addition, the virtual objects and dynamic paths are placed by using a smartphone in a real-world space. To implement this system, three cored technologies are needed: (i) segmentation of regions of spaces and real-world objects (ii) hand tracking and gesture recognition for manipulating virtual objects, (iii) dynamic path placing of virtual objects using a smartphone wearing an OST HMD. We implement a prototype of the proposed system for testing its feasibility. To the end, we expect that our system enables simplify AR technology usage to ordinary users who are unfamiliar with professional AR authoring tools.

We presents a method to generate and track AR space using plane geometry information in AR environment. Our method calculates the major plane correspondences between frames and defines the relation. Using this relationship, we can create the virtual space that is unaffected by object position changes in images and can be used for AR tracking.

고차원 특징 구성을 위한 네트워크 기반 모바일 슬램 시스템 및 그 동작 방법이 제시된다. 본 발명의 일 실시예에 따른 고차원 특징 구성을 위한 네트워크 기반 모바일 슬램 시스템의 동작 방법은, 네트워크 모듈을 통해 고차원 특징을 구성하기 위한 입력 값을 서비스 모듈로 전달하는 단계; 상기 서비스 모듈에서 상기 입력 값을 처리하고 결과 값을 반환하는 단계; 상기 네트워크 모듈에서 상기 서비스 모듈로부터 상기 결과 값을 전송 받아 변환 모듈로 전달하는 단계; 상기 변환 모듈에서 전달 받은 상기 결과 값을 취합하여 주변 환경을 인지하기 위한 고차원 특징을 구성하는 단계; 및 슬램 모듈은 상기 고차원 특징을 전달 받아 동적으로 변화하는 실제 환경을 반영하여 지도를 구성하는 단계를 포함하여 이루어질 수 있다.

본 발명은 가시광 통신(Li‐Fi)을 이용하여 특정 조명구역에서의 키프레임 정보 및 공간정보를 제공하며, 사용자의 모바일 디바이스를 통해 조명구역에서의 증강현실 체험을 제공하는 가시광 통신을 이용한 실내공간의 증강현실 제공 시스템 및 그 방법에 관한 것으로, 정확한 실내 공간의 복원뿐만 아니라 사용자의 위치 및 모바일 디바이스의 자세를 정확하게 추정할 수 있다.

3D Gaussian Splatting (3DGS) enables photorealistic novel-view synthesis with real-time rendering, but deploying compressed object-centric 3DGS in XR requires more than image-space rate–distortion. In practical XR asset pipelines, reusable objects are repeatedly packaged, transmitted, decoded, and instantiated, making asset-preparation cost, codec compatibility, decoding latency, and preservation of depth and silhouette cues first-class concerns. Existing 3DGS compression methods are largely developed for scene-scale captures and often rely on heavy layout generation or aggressive global pruning, assumptions that transfer poorly to semantically concentrated foreground objects. We present AtlasLC, a source-free, training-free compression pipeline for object-centric 3DGS that operates directly on released Gaussian assets, without original images, camera poses, or per-asset optimization. AtlasLC couples local-competition pruning with deterministic atlas packing to remove the mapping/remapping bottleneck while preserving object-wide foreground support; a lightweight single-pass sort-based conditional transport is used as a shared coordinate backbone for these stages. Across the evaluated assets, AtlasLC reduces atlas-preparation time by up to 25× and end-to-end compression time by up to 5×, while offering a favorable deployment-aware balance of payload, decode latency, runtime FPS, and 3D geometry relative to the evaluated compressed baselines. Relative to similarly compact structured baselines, it uses about 6–8% fewer bits while maintaining comparable perceptual and geometric quality. These results show that object-centric 3DGS compression should be optimized for a deployment-aware operating point enabling scalable XR asset libraries.

We propose a robust 3D hand tracking system in various hand action environments, including hand-object interaction, which utilizes a single color image and a previous pose prediction as input. We observe that existing methods deterministically exploit temporal information in motion space, failing to address realistic diverse hand motions. Also, prior methods paid less attention to efficiency as well as robust performance, i.e., the balance issues between time and accuracy. The Temporally Enhanced Graph Convolutional Network (TE-GCN) utilizes a 2-stage framework to encode temporal information adaptively. The system establishes balance by adopting an adaptive GCN, which effectively learns the spatial dependency between hand mesh vertices. Furthermore, the system leverages the previous prediction by estimating the relevance across image features through the attention mechanism. The proposed method achieves state-of-the-art balanced performance on challenging benchmarks and demonstrates robust results on various hand motions in real scenes. Moreover, the hand tracking system is integrated into a recent HMD with an off-loading framework, achieving a real-time framerate while maintaining high performance. Our study improves the usability of a high-performance hand-tracking method, which can be generalized to other algorithms and contributes to the usage of HMD in everyday life. Our code with the HMD project will be available at https://github.com/UVR-WJCHO/TEGCN_on_Hololens2 .