Self-Supervised Learning and 3D Printing Technology in Facial Reconstruction and Defect Coverage

被引:0
|
作者
Tung, N. T. [1 ]
Chau, Nguyen Dong [2 ]
Nguyen, Nghi N. [3 ]
Nguyen, Thanh Q. [4 ]
机构
[1] HUTECH Univ, Fac Informat Technol, Ho Chi Minh City, Vietnam
[2] Ton Duc Thang Univ, Fac Informat Technol, Ho Chi Minh City, Vietnam
[3] Hosp Odontostomatol Ho Chi Minh City, Ho Chi Minh City, Vietnam
[4] Nguyen Tat Thanh Univ, Inst Interdisciplinary Social Sci, 2 Vo Oanh St,Ward 25, Ho Chi Minh City 700000, Vietnam
关键词
3D printing technology; facial reconstruction; self-supervised learning; 3D printing model; FACE;
D O I
10.1089/3dp.2024.0221
中图分类号
T [工业技术];
学科分类号
08 ;
摘要
This study proposes a model for creating facial wound-covering masks to support patients recovering from injuries, especially those with scars or deformities resulting from accidents and wars. The model aims to increase patient confidence, improve wound hygiene, and protect the environment. A novel dataset was developed, consisting of diverse facial images with various scar conditions simulated from real human scars. The study employs self-supervised learning (SSL) with a pretrained base model to convert 2D images into 3D representations without compromising critical facial features. SSL is implemented during the encoding phase, allowing the model to familiarize itself with new data. Through the integration of 3D printing technology, the entire process, from wound reconstruction to product manufacturing, has been tested in the laboratory. The results indicate that the model not only effectively covers wounds but also restores the original facial structure nearly perfectly. The improvement is clearly demonstrated through error reduction and increased accuracy across experiments with diverse datasets. This research opens new possibilities for practical applications, particularly for war victims, by offering a novel, safe, and convenient treatment solution.
引用
收藏
页数:19
相关论文
共 50 条
  • [1] Self-Supervised Learning of Detailed 3D Face Reconstruction
    Chen, Yajing
    Wu, Fanzi
    Wang, Zeyu
    Song, Yibing
    Ling, Yonggen
    Bao, Linchao
    IEEE TRANSACTIONS ON IMAGE PROCESSING, 2020, 29 : 8696 - 8705
  • [2] Multi-view self-supervised learning for 3D facial texture reconstruction from single image
    Zeng, Xiaoxing
    Hu, Ruyun
    Shi, Wu
    Qiao, Yu
    IMAGE AND VISION COMPUTING, 2021, 115
  • [3] Consistent 3D Hand Reconstruction in Video via Self-Supervised Learning
    Tu, Zhigang
    Huang, Zhisheng
    Chen, Yujin
    Kang, Di
    Bao, Linchao
    Yang, Bisheng
    Yuan, Junsong
    IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE, 2023, 45 (08) : 9469 - 9485
  • [4] Enhancing Face Recognition with Self-Supervised 3D Reconstruction
    He, Mingjie
    Zhang, Jie
    Shan, Shiguang
    Chen, Xilin
    2022 IEEE/CVF CONFERENCE ON COMPUTER VISION AND PATTERN RECOGNITION (CVPR 2022), 2022, : 4052 - 4061
  • [5] Model-based 3D Hand Reconstruction via Self-Supervised Learning
    Chen, Yujin
    Tu, Zhigang
    Kang, Di
    Bao, Linchao
    Zhang, Ying
    Zhe, Xuefei
    Chen, Ruizhi
    Yuan, Junsong
    2021 IEEE/CVF CONFERENCE ON COMPUTER VISION AND PATTERN RECOGNITION, CVPR 2021, 2021, : 10446 - 10455
  • [6] Hybrid Supervised and Self-Supervised Learning for 3D Printing Optimization: A Masked Supervised Bootstrap Your Own Latent Approach
    Nguyen, Phuong Dong
    Dao, Manh Binh
    Nguyen, Thanh Q.
    3D PRINTING AND ADDITIVE MANUFACTURING, 2025,
  • [7] 3D Human Pose Machines with Self-Supervised Learning
    Wang, Keze
    Lin, Liang
    Jiang, Chenhan
    Qian, Chen
    Wei, Pengxu
    IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE, 2020, 42 (05) : 1069 - 1082
  • [8] Visual Reinforcement Learning With Self-Supervised 3D Representations
    Ze, Yanjie
    Hansen, Nicklas
    Chen, Yinbo
    Jain, Mohit
    Wang, Xiaolong
    IEEE ROBOTICS AND AUTOMATION LETTERS, 2023, 8 (05) : 2890 - 2897
  • [9] Self-Supervised Online Learning of Appearance for 3D Tracking
    Lee, Bhoram
    Lee, Daniel D.
    2017 IEEE/RSJ INTERNATIONAL CONFERENCE ON INTELLIGENT ROBOTS AND SYSTEMS (IROS), 2017, : 4930 - 4937
  • [10] Self-Supervised Deep Learning for 3D Gravity Inversion
    Li, Yinshuo
    Jia, Zhuo
    Lu, Wenkai
    IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING, 2022, 60