# 3D Dental Model Segmentation with Geometrical Boundary Preserving

arXiv: https://arxiv.org/abs/2503.23702 · HTML: https://mlsanity.com/paper/2503.23702/

## 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 (most cited first, 19 of 19)

- [Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks](https://mlsanity.com/paper/1506.01497/) (2015)
- [PointNet: Deep Learning on Point Sets for 3D Classification and Segmentation](https://mlsanity.com/paper/1612.00593/) (2016)
- [PointNet++: Deep Hierarchical Feature Learning on Point Sets in a Metric Space](https://mlsanity.com/paper/1706.02413/) (2017)
- [Pyramid Scene Parsing Network](https://mlsanity.com/paper/1612.01105/) (2016)
- [Dynamic Graph CNN for Learning on Point Clouds](https://mlsanity.com/paper/1801.07829/) (2018)
- [Point Transformer](https://mlsanity.com/paper/2011.00931/) (2020)
- [PointCNN: Convolution On X-Transformed Points](https://mlsanity.com/paper/1801.07791/) (2018)
- [2DPASS: 2D Priors Assisted Semantic Segmentation on LiDAR Point Clouds](https://mlsanity.com/paper/2207.04397/) (2022)
- [JSENet: Joint Semantic Segmentation and Edge Detection Network for 3D Point Clouds](https://mlsanity.com/paper/2007.06888/) (2020)
- [Contrastive Boundary Learning for Point Cloud Segmentation](https://mlsanity.com/paper/2203.05272/) (2022)
- [Two-Stage Mesh Deep Learning for Automated Tooth Segmentation and Landmark Localization on 3D Intraoral Scans](https://mlsanity.com/paper/2109.11941/) (2021)
- [Boundary-Aware Geometric Encoding for Semantic Segmentation of Point Clouds](https://mlsanity.com/paper/2101.02381/) (2021)
- [DArch: Dental Arch Prior-assisted 3D Tooth Instance Segmentation](https://mlsanity.com/paper/2204.11911/) (2022)
- [TSGCNet: Discriminative Geometric Feature Learning with Two-Stream GraphConvolutional Network for 3D Dental Model Segmentation](https://mlsanity.com/paper/2012.13697/) (2020)
- [3DTeethSeg'22: 3D Teeth Scan Segmentation and Labeling Challenge](https://mlsanity.com/paper/2305.18277/) (2023)
- [An Implicit Parametric Morphable Dental Model](https://mlsanity.com/paper/2211.11402/) (2022)
- [TSegFormer: 3D Tooth Segmentation in Intraoral Scans with Geometry Guided Transformer](https://mlsanity.com/paper/2311.13234/) (2023)
- [3D Tooth Mesh Segmentation with Simplified Mesh Cell Representation](https://mlsanity.com/paper/2301.10531/) (2023)
- [Cross-modal & Cross-domain Learning for Unsupervised LiDAR Semantic Segmentation](https://mlsanity.com/paper/2308.02883/) (2023)

## Built on by (most cited first, 7 of 7)

- [ArchMap: Arch-Flattening and Knowledge-Guided Vision Language Model for Tooth Counting and Structured Dental Understanding](https://mlsanity.com/paper/2511.14336/) (2025)
- [TSegAgent: Zero-Shot Tooth Segmentation via Geometry-Aware Vision-Language Agents](https://mlsanity.com/paper/2603.19684/) (2026)
- [SOFTooth: Semantics-Enhanced Order-Aware Fusion for Tooth Instance Segmentation](https://mlsanity.com/paper/2512.23411/) (2025)
- [Prompt-Based Caption Generation for Single-Tooth Dental Images Using Vision-Language Models](https://mlsanity.com/paper/2603.07403/) (2026)
- [Text-Conditioned Multi-Expert Regression Framework for Fully Automated Multi-Abutment Design](https://mlsanity.com/paper/2604.09047/) (2026)
- [Topology-Constrained Quantized nnUNet for Efficient and Anatomically Accurate 3D Tooth Segmentation](https://mlsanity.com/paper/2605.04201/) (2026)
- [Augmented Equivariant Mesh Networks for Anatomical Segmentation](https://mlsanity.com/paper/2605.08172/) (2026)

## 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.