mlsanityData to 14 Oct 2026

Transformer-Based Tooth Alignment Prediction With Occlusion And Collision Constraints

arXiv 2410.20806 ↗First posted 28 Oct 2024cs.CVcited by 0

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Abstract (arXiv)

The planning of digital orthodontic treatment requires providing tooth alignment, which not only consumes a lot of time and labor to determine manually but also relays clinical experiences heavily. In this work, we proposed a lightweight tooth alignment neural network based on Swin-transformer. We first re-organized 3D point clouds based on virtual arch lines and converted them into order-sorted multi-channel textures, which improves the accuracy and efficiency simultaneously. We then designed two new occlusal loss functions that quantitatively evaluate the occlusal relationship between the upper and lower jaws. They are important clinical constraints, first introduced to the best of our knowledge, and lead to cutting-edge prediction accuracy. To train our network, we collected a large digital orthodontic dataset that has 591 clinical cases, including various complex clinical cases. This dataset will benefit the community after its release since there is no open dataset so far. Furthermore, we also proposed two new orthodontic dataset augmentation methods considering tooth spatial distribution and occlusion. We evaluated our method with this dataset and extensive experiments, including comparisons with STAT methods and ablation studies, and demonstrate the high prediction accuracy of our method.

Key facts. No citing papers in the map yet (data through 14 Oct 2026). Builds on 22 of its 56 references that are in the map. First posted on arXiv 28 Oct 2024.

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22 of 56 references.

References · 22

  1. 2017Attention Is All You Needcited by 60,361
  2. 2020An Image is Worth 16x16 Words: Transformers for Image Recognition at Scalecited by 30,186
  3. 2020Denoising Diffusion Probabilistic Modelscited by 21,076
  4. 2021Swin Transformer: Hierarchical Vision Transformer using Shifted Windowscited by 12,139
  5. 2015Deep Unsupervised Learning using Nonequilibrium Thermodynamicscited by 7,058
  6. 2018A Style-Based Generator Architecture for Generative Adversarial Networkscited by 6,013
  7. 2016PointNet: Deep Learning on Point Sets for 3D Classification and Segmentationcited by 5,248
  8. 2017Squeeze-and-Excitation Networkscited by 4,789
  9. 2017PointNet++: Deep Hierarchical Feature Learning on Point Sets in a Metric Spacecited by 4,401
  10. 2013Distributed Representations of Words and Phrases and their Compositionalitycited by 3,474
  11. 2018Dynamic Graph CNN for Learning on Point Cloudscited by 2,223
  12. 2020Point Transformercited by 1,470
10 more
  1. 2023Point Transformer V3: Simpler, Faster, Strongercited by 589
  2. 2019Normalized Object Coordinate Space for Category-Level 6D Object Pose and Size Estimationcited by 495
  3. 2022Point Transformer V2: Grouped Vector Attention and Partition-based Poolingcited by 388
  4. 2018SO-Net: Self-Organizing Network for Point Cloud Analysiscited by 210
  5. 2023Swin3D: A Pretrained Transformer Backbone for 3D Indoor Scene Understandingcited by 113
  6. 2023OctFormer: Octree-based Transformers for 3D Point Cloudscited by 109
  7. 2023FlatFormer: Flattened Window Attention for Efficient Point Cloud Transformercited by 73
  8. 2021Two-Stage Mesh Deep Learning for Automated Tooth Segmentation and Landmark Localization on 3D Intraoral Scanscited by 24
  9. 2021Multi-scale Graph Convolutional Networks with Self-Attentioncited by 3
  10. 2023Automatic Tooth Arrangement with Joint Features of Point and Mesh Representations via Diffusion Probabilistic Modelscited by 3

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