# TSGCNet: Discriminative Geometric Feature Learning with Two-Stream GraphConvolutional Network for 3D Dental Model Segmentation

arXiv: https://arxiv.org/abs/2012.13697 · HTML: https://mlsanity.com/paper/2012.13697/

## Key facts

- Cited by 16 papers in the map (data through 7 Oct 2026).
- First posted on arXiv 26 Dec 2020.

## Built on by (most cited first, 16 of 16)

- [DArch: Dental Arch Prior-assisted 3D Tooth Instance Segmentation](https://mlsanity.com/paper/2204.11911/) (2022)
- [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)
- [Two-Stream Graph Convolutional Network for Intra-oral Scanner Image Segmentation](https://mlsanity.com/paper/2204.08797/) (2022)
- [3D Dental Model Segmentation with Geometrical Boundary Preserving](https://mlsanity.com/paper/2503.23702/) (2025)
- [3D Tooth Mesh Segmentation with Simplified Mesh Cell Representation](https://mlsanity.com/paper/2301.10531/) (2023)
- [Automatic Tooth Segmentation from 3D Dental Model using Deep Learning: A Quantitative Analysis of what can be learnt from a Single 3D Dental Model](https://mlsanity.com/paper/2209.08132/) (2022)
- [AI-Enhanced Virtual Reality in Medicine: A Comprehensive Survey](https://mlsanity.com/paper/2402.03093/) (2024)
- [Teeth-SEG: An Efficient Instance Segmentation Framework for Orthodontic Treatment based on Anthropic Prior Knowledge](https://mlsanity.com/paper/2404.01013/) (2024)
- [TFormer: 3D Tooth Segmentation in Mesh Scans with Geometry Guided Transformer](https://mlsanity.com/paper/2210.16627/) (2022)
- [A Critical Analysis of the Limitation of Deep Learning based 3D Dental Mesh Segmentation Methods in Segmenting Partial Scans](https://mlsanity.com/paper/2305.00244/) (2023)
- [3DTeethSAM: Taming SAM2 for 3D Teeth Segmentation](https://mlsanity.com/paper/2512.11557/) (2025)
- [TSegAgent: Zero-Shot Tooth Segmentation via Geometry-Aware Vision-Language Agents](https://mlsanity.com/paper/2603.19684/) (2026)
- [OrthoAI: A Neurosymbolic Framework for Evidence-Grounded Biomechanical Reasoning in Clear Aligner Orthodontics](https://mlsanity.com/paper/2603.00124/) (2026)
- [TCATSeg: A Tooth Center-Wise Attention Network for 3D Dental Model Semantic Segmentation](https://mlsanity.com/paper/2603.16620/) (2026)
- [ProtoPointNet: Prototype-Based Interpretable Classification of 3D Dental Point Clouds with Verifiable Spatial Activations](https://mlsanity.com/paper/2607.12335/) (2026)

## Abstract (arXiv)

The ability to segment teeth precisely from digitized 3D dental models is an essential task in computer-aided orthodontic surgical planning. To date, deep learning based methods have been popularly used to handle this task. State-of-the-art methods directly concatenate the raw attributes of 3D inputs, namely coordinates and normal vectors of mesh cells, to train a single-stream network for fully-automated tooth segmentation. This, however, has the drawback of ignoring the different geometric meanings provided by those raw attributes. This issue might possibly confuse the network in learning discriminative geometric features and result in many isolated false predictions on the dental model. Against this issue, we propose a two-stream graph convolutional network (TSGCNet) to learn multi-view geometric information from different geometric attributes. Our TSGCNet adopts two graph-learning streams, designed in an input-aware fashion, to extract more discriminative high-level geometric representations from coordinates and normal vectors, respectively. These feature representations learned from the designed two different streams are further fused to integrate the multi-view complementary information for the cell-wise dense prediction task. We evaluate our proposed TSGCNet on a real-patient dataset of dental models acquired by 3D intraoral scanners, and experimental results demonstrate that our method significantly outperforms state-of-the-art methods for 3D shape segmentation.