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 gen
erative 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 im
prove performance: changing the uniformity of the point cloud distribution, varying the number of sam
pled 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
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- 대표 발명자
- 전경구
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- 출원번호
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10-2025-0005774
(2025-01-15)