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@Article{app15031501,
AUTHOR = {Park, Sieun and Jeong, Won-Je and Manawadu, Mayura and Park, Soon-Yong},
TITLE = {6-DoF Pose Estimation from Single RGB Image and CAD Model Retrieval Using Feature Similarity Measurement},
JOURNAL = {Applied Sciences},
VOLUME = {15},
YEAR = {2025},
NUMBER = {3},
ARTICLE-NUMBER = {1501},
URL = {https://www.mdpi.com/2076-3417/15/3/1501},
ISSN = {2076-3417},
ABSTRACT = {This study presents six degrees of freedom (6-DoF) pose estimation of an object from a single RGB image and retrieval of the matching CAD model by measuring the similarity between RGB and CAD rendering images. The 6-DoF pose estimation of an RGB object is one of the important techniques in 3D computer vision. However, in addition to 6-DoF pose estimation, retrieval and alignment of the matching CAD model with the RGB object should be performed for various industrial applications such as eXtended Reality (XR), Augmented Reality (AR), robot’s pick and place, and so on. This paper addresses 6-DoF pose estimation and CAD model retrieval problems simultaneously and quantitatively analyzes how much the 6-DoF pose estimation affects the CAD model retrieval performance. This study consists of two main steps. The first step is 6-DoF pose estimation based on the PoseContrast network. We enhance the structure of PoseConstrast by adding variance uncertainty weight and feature attention modules. The second step is the retrieval of the matching CAD model by an image similarity measurement between the CAD rendering and the RGB object. In our experiments, we used 2000 RGB images collected from Google and Bing search engines and 100 CAD models from ShapeNetCore. The Pascal3D+ dataset is used to train the pose estimation network and DELF features are used for the similarity measurement. Comprehensive ablation studies about the proposed network show the quantitative performance analysis with respect to the baseline model. Experimental results show that the pose estimation performance has a positive correlation with the CAD retrieval performance.},
DOI = {10.3390/app15031501}
}

@article{park2023relative,
title={ Relative Pose Estimation between Image Object and ShapeNet CAD Model for Automatic 4-DoF Annotation },
author={Park, Soon-Yong and Son, Chang-Min and Jeong, Won-Jae and Park, Sieun},
journal={Applied Sciences},
volume={13},
number={2},
pages={693},
year={2023},
publisher={Multidisciplinary Digital Publishing Institute},
doi={10.3390/app13020693}
}

@inproceedings{park20246,
title={6-DoF Pose Estimation and CAD Model Retrieval for XR Interface from a Single RGB Image},
author={Park, Sieun and Jeong, Wonje and Park, Soon-Yong},
booktitle={Proceedings of the 2024 International Conference on Advanced Visual Interfaces},
pages={1--3},
year={2024},
doi={10.1145/3656650.3656719}
}

@domestic_conference{Mayura2024advancing,
title={Advancing 6D Pose Estimation in Augmented Reality - Overcoming Projection Ambiguity with Uncontrolled Imagery},
author={Manawadu, Mayura and Park, Sieun and Park, Soon-Yong},
journal={IPIU},
year={2024},
}

@domestic_conference{Park2024quaternion,
title={쿼터니언 손실함수를 이용한 물체 포즈 추정 딥러닝 네트워크의 성능 개선},
author={Park, Sieun and Park, Soon-Yong},
journal={IPIU},
year={2024},
}

@domestic_conference{Park2023DELF,
title={DELF 심층 네트워크를 이용한 서로 다른 도메인에서의 이미지 유사성 비교},
author={Park, Sieun and Park, Soon-Yong},
journal={IPIU},
year={2023},
}