# Teeth3DS dataset

The work proposes TSegAgent, which reformulates tooth instance segmentation and FDI labeling of intra-oral scanned 3D models as a zero-shot geometric reasoning problem: multi-view renderings with curvature heatmaps and the SAM3 text prompt "tooth" produce candidate masks, which are merged into face-level instance labels via IoU and containment relations, after which a geometry-aware vision-language agent performs non-tooth region identification, central incisor localization, full-arch classification, and error correction through multi-round conversation; it reports mIoU 93.37, TLA 96.40, TSA 96.76, TIR 97.46, and TIR=1 87.17 on Teeth3DS with 1200 3D tooth models, and mIoU 82.10, TLA 96.68, TSA 95.99, TIR 85.41, and TIR=1 51.

The work proposes TSegAgent, which reformulates tooth instance segmentation and FDI labeling of intra-oral scanned 3D models as a zero-shot geometric reasoning problem: multi-view renderings with curvature heatmaps and the SAM3 text prompt "tooth" produce candidate masks, which are merged into face-level instance labels via IoU and containment relations, after which a geometry-aware vision-language agent performs non-tooth region identification, central incisor localization, full-arch classification, and error correction through multi-round conversation; it reports mIoU 93.37, TLA 96.40, TSA 96.76, TIR 97.46, and TIR=1 87.17 on Teeth3DS with 1200 3D tooth models, and mIoU 82.10, TLA 96.68, TSA 95.99, TIR 85.41, and TIR=1 51.

The study proposes a software-only method that reconstructs a 3D oral model from only ten 2D intraoral images captured from different angles, requiring no dedicated hardware; the model is trained on the public Teeth3DS dataset of 950 upper jaw samples and uses MobileNetV2 as the image encoder with Multi-head Attention for multi-view feature fusion, achieving 77.49% accuracy under nearest-neighbor matching with a distance threshold of 0.035, while predicted vertices tend to concentrate in high-density regions of the ground truth, producing uneven point distribution in the reconstructed model.

The study proposes a software-only method that reconstructs a 3D oral model from only ten 2D intraoral images captured from different angles, requiring no dedicated hardware; the model is trained on the public Teeth3DS dataset of 950 upper jaw samples and uses MobileNetV2 as the image encoder with Multi-head Attention for multi-view feature fusion, achieving 77.49% accuracy under nearest-neighbor matching with a distance threshold of 0.035, while predicted vertices tend to concentrate in high-density regions of the ground truth, producing uneven point distribution in the reconstructed model.