mlsanityData to 14 Oct 2026

Leveraging Point Transformers for Detecting Anatomical Landmarks in Digital Dentistry

arXiv 2504.11418 ↗First posted 15 Apr 2025cs.CVcited by 2

Not on the map. Why?

Abstract (arXiv)

The increasing availability of intraoral scanning devices has heightened their importance in modern clinical orthodontics. Clinicians utilize advanced Computer-Aided Design techniques to create patient-specific treatment plans that include laboriously identifying crucial landmarks such as cusps, mesial-distal locations, facial axis points, and tooth-gingiva boundaries. Detecting such landmarks automatically presents challenges, including limited dataset sizes, significant anatomical variability among subjects, and the geometric nature of the data. We present our experiments from the 3DTeethLand Grand Challenge at MICCAI 2024. Our method leverages recent advancements in point cloud learning through transformer architectures. We designed a Point Transformer v3 inspired module to capture meaningful geometric and anatomical features, which are processed by a lightweight decoder to predict per-point distances, further processed by graph-based non-minima suppression. We report promising results and discuss insights on learned feature interpretability.

Key facts. Cited by 2 papers in the map (data through 14 Oct 2026). Builds on 24 of its 37 references that are in the map. First posted on arXiv 15 Apr 2025.

Builds on

24 of 37 references.

References · 24

  1. 2016PointNet: Deep Learning on Point Sets for 3D Classification and Segmentationcited by 5,248
  2. 2017PointNet++: Deep Hierarchical Feature Learning on Point Sets in a Metric Spacecited by 4,401
  3. 2020Point Transformercited by 1,470
  4. 2021Geometric Deep Learning: Grids, Groups, Graphs, Geodesics, and Gaugescited by 1,167
  5. 2018PointCNN: Convolution On X-Transformed Pointscited by 764
  6. 2018PointConv: Deep Convolutional Networks on 3D Point Cloudscited by 657
  7. 2021Point-BERT: Pre-training 3D Point Cloud Transformers with Masked Point Modelingcited by 653
  8. 2023Point Transformer V3: Simpler, Faster, Strongercited by 589
  9. 2015Multi-view Convolutional Neural Networks for 3D Shape Recognitioncited by 565
  10. 2022Point Transformer V2: Grouped Vector Attention and Partition-based Poolingcited by 388
  11. 2023VoxelNeXt: Fully Sparse VoxelNet for 3D Object Detection and Trackingcited by 222
  12. 2020DiffusionNet: Discretization Agnostic Learning on Surfacescited by 164
12 more
  1. 2023OctFormer: Octree-based Transformers for 3D Point Cloudscited by 109
  2. 2021Subdivision-Based Mesh Convolution Networkscited by 60
  3. 2022MeshMAE: Masked Autoencoders for 3D Mesh Data Analysiscited by 31
  4. 2021Two-Stage Mesh Deep Learning for Automated Tooth Segmentation and Landmark Localization on 3D Intraoral Scanscited by 24
  5. 20233DTeethSeg'22: 3D Teeth Scan Segmentation and Labeling Challengecited by 14
  6. 2020A Survey of Algorithms for Geodesic Paths and Distancescited by 12
  7. 2022Laplacian2Mesh: Laplacian-Based Mesh Understandingcited by 11
  8. 2022CEPHA29: Automatic Cephalometric Landmark Detection Challenge 2023cited by 6
  9. 2020Automatic Recognition of Landmarks on Digital Dental Modelscited by 4
  10. 2021Dense Representative Tooth Landmark/axis Detection Network on 3D Modelcited by 4
  11. 2020Robust Object Classification Approach using Spherical Harmonicscited by 2
  12. 2023EchoGLAD: Hierarchical Graph Neural Networks for Left Ventricle Landmark Detection on Echocardiogramscited by 2

Built on by

Cited by year

Citing papers per year: 2025: 2.

2 papers.

  1. 2025PAL-Net: A Point-Wise CNN with Patch-Attention for 3D Facial Landmark Localizationcited by 0
  2. 2025Detecting Dental Landmarks from Intraoral 3D Scans: the 3DTeethLand challengecited by 0