지원사업
학술연구/단체지원/교육 등 연구자 활동을 지속하도록 DBpia가 지원하고 있어요.
커뮤니티
연구자들이 자신의 연구와 전문성을 널리 알리고, 새로운 협력의 기회를 만들 수 있는 네트워킹 공간이에요.
논문 기본 정보
- 자료유형
- 학술저널
- 저자정보
- 발행연도
- 2024.2
- 수록면
- 250 - 256 (7page)
- DOI
- 10.9717/kmms.2024.27.2.250
이용수
초록· 키워드
오류제보하기
Teeth vary in size and shape among individuals, making the production of dental prosthetics with consistent quality challenging. Additionally, the quality of dental prosthetics is influenced by the manual skill level of dental technicians who create them. To improve this, recent studies have utilized generative adversarial networks (GANs) to create dental prosthetics. With 2D GAN models, a problem arises when converting 3D dental data into 2D data, resulting in the loss of spatial features. Point cloud completion models, which use 3D data as input, offer a partial solution to this problem. The performance of point cloud completion models varies depending on the representation and information contained in the input data. With this in mind, this study proposes three methods for processing input data to improve performance: changing the uniformity of the point cloud distribution, varying the number of sampled points, and including occlusal information with adjacent teeth. Experiments were conducted to compare and analyze performance changes based on these input data modifications. The results showed that there was up to a 30% performance difference in representative point cloud completion models, SnowFlakeNet and SeedFormer, based on CD (Chamfer Distance) Loss.
목차
ABSTRACT
1. 서론
2. 관련 연구
3. 제안 방법
4. 실험
5. 결론
REFERENCE
참고문헌 (19)
참고문헌 신청
S. Lee, K. Son, J. Park, J. Lee, S.H. Kang, R.E. Wijesinghe, et al., “Non-Ionized, High-Resolution Measurement of Internal and Marginal Discrepancies of Dental Prosthesis Using Optical Coherence Tomography,” IEEE Access, No. 7, pp. 6209-6218, 2018.
I. Goodfellow, J. Pouget-Abadie, M. Mirza, B. Xu, W. David, S. Ozair, et al., “Generative Adversarial Networks,” Commun ACM, Vol. 63, No. 11, pp. 139-144, 2020.
H. Shin, D. Kim, S. Chang, K. Kim, J. Jeong, Y. Son, “A Study on Generation Model of DCGANs-Based Teeth Image Data for Effective Identification,” Journal of Korean Institute of Intelligent Systems, Vol. 30, No. 4, pp. 265-271, 2020.
S. Tian, M. Wang, N. Dai, H. Ma, L. Li, L. Fiorenza, et al., “DCPR-GAN: Dental Crown Prosthesis Restoration Using Two-Stage Generative Adversarial Networks,” IEEE Journal of Biomedical and Health Informatics, Vol. 26, No. 1, pp. 151-160, 2021.
H. Lee, N. Thoummala, H. Park, S. Ham, and J. Yu, “AI-Based Dental Prostheses Fabrication Using Generative Adversarial Networks,” Quantitative Bio-Science, Vol. 40, No. 1, pp. 39-44, 2021.
I. Goodfellow, J. Pouget-Abadie, M. Mirza, B. Xu, W. David, S. Ozair, et al., “Generative Adversarial Networks,” Commun ACM, Vol. 63, No. 11, pp. 139-144, 2020.
H. Shin, D. Kim, S. Chang, K. Kim, J. Jeong, Y. Son, “A Study on Generation Model of DCGANs-Based Teeth Image Data for Effective Identification,” Journal of Korean Institute of Intelligent Systems, Vol. 30, No. 4, pp. 265-271, 2020.
S. Tian, M. Wang, N. Dai, H. Ma, L. Li, L. Fiorenza, et al., “DCPR-GAN: Dental Crown Prosthesis Restoration Using Two-Stage Generative Adversarial Networks,” IEEE Journal of Biomedical and Health Informatics, Vol. 26, No. 1, pp. 151-160, 2021.
H. Lee, N. Thoummala, H. Park, S. Ham, and J. Yu, “AI-Based Dental Prostheses Fabrication Using Generative Adversarial Networks,” Quantitative Bio-Science, Vol. 40, No. 1, pp. 39-44, 2021.
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