mlsanityData to 14 Oct 2026

3D Dental Model Segmentation with Geometrical Boundary Preserving

arXiv 2503.23702 ↗First posted 31 Mar 2025cs.CVcited by 7

Not on the map. Why?

Abstract (arXiv)

3D intraoral scan mesh is widely used in digital dentistry diagnosis, segmenting 3D intraoral scan mesh is a critical preliminary task. Numerous approaches have been devised for precise tooth segmentation. Currently, the deep learning-based methods are capable of the high accuracy segmentation of crown. However, the segmentation accuracy at the junction between the crown and the gum is still below average. Existing down-sampling methods are unable to effectively preserve the geometric details at the junction. To address these problems, we propose CrossTooth, a boundary-preserving segmentation method that combines 3D mesh selective downsampling to retain more vertices at the tooth-gingiva area, along with cross-modal discriminative boundary features extracted from multi-view rendered images, enhancing the geometric representation of the segmentation network. Using a point network as a backbone and incorporating image complementary features, CrossTooth significantly improves segmentation accuracy, as demonstrated by experiments on a public intraoral scan dataset.

Key facts. Cited by 7 papers in the map (data through 14 Oct 2026). Builds on 19 of its 49 references that are in the map. First posted on arXiv 31 Mar 2025.

Builds on

19 of 49 references.

References · 19

  1. 2015Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networkscited by 9,639
  2. 2016PointNet: Deep Learning on Point Sets for 3D Classification and Segmentationcited by 5,248
  3. 2017PointNet++: Deep Hierarchical Feature Learning on Point Sets in a Metric Spacecited by 4,401
  4. 2016Pyramid Scene Parsing Networkcited by 2,479
  5. 2018Dynamic Graph CNN for Learning on Point Cloudscited by 2,223
  6. 2020Point Transformercited by 1,470
  7. 2018PointCNN: Convolution On X-Transformed Pointscited by 764
  8. 20222DPASS: 2D Priors Assisted Semantic Segmentation on LiDAR Point Cloudscited by 158
  9. 2020JSENet: Joint Semantic Segmentation and Edge Detection Network for 3D Point Cloudscited by 62
  10. 2022Contrastive Boundary Learning for Point Cloud Segmentationcited by 56
  11. 2021Two-Stage Mesh Deep Learning for Automated Tooth Segmentation and Landmark Localization on 3D Intraoral Scanscited by 24
  12. 2021Boundary-Aware Geometric Encoding for Semantic Segmentation of Point Cloudscited by 23
7 more
  1. 2022DArch: Dental Arch Prior-assisted 3D Tooth Instance Segmentationcited by 18
  2. 2020TSGCNet: Discriminative Geometric Feature Learning with Two-Stream GraphConvolutional Network for 3D Dental Model Segmentationcited by 16
  3. 20233DTeethSeg'22: 3D Teeth Scan Segmentation and Labeling Challengecited by 14
  4. 2022An Implicit Parametric Morphable Dental Modelcited by 12
  5. 2023TSegFormer: 3D Tooth Segmentation in Intraoral Scans with Geometry Guided Transformercited by 10
  6. 20233D Tooth Mesh Segmentation with Simplified Mesh Cell Representationcited by 4
  7. 2023Cross-modal & Cross-domain Learning for Unsupervised LiDAR Semantic Segmentationcited by 4

Built on by

Cited by year (2026 to 14 Oct)

Citing papers per year: 2025: 2; 2026: 5.

7 papers.

  1. 2025ArchMap: Arch-Flattening and Knowledge-Guided Vision Language Model for Tooth Counting and Structured Dental Understandingcited by 2
  2. 2026TSegAgent: Zero-Shot Tooth Segmentation via Geometry-Aware Vision-Language Agentscited by 1
  3. 2025SOFTooth: Semantics-Enhanced Order-Aware Fusion for Tooth Instance Segmentationcited by 0
  4. 2026Prompt-Based Caption Generation for Single-Tooth Dental Images Using Vision-Language Modelscited by 0
  5. 2026Text-Conditioned Multi-Expert Regression Framework for Fully Automated Multi-Abutment Designcited by 0
  6. 2026Topology-Constrained Quantized nnUNet for Efficient and Anatomically Accurate 3D Tooth Segmentationcited by 0
  7. 2026Augmented Equivariant Mesh Networks for Anatomical Segmentationcited by 0