AAAI 2026technical0 citations
3DTeethSAM: Taming SAM2 for 3D Teeth Segmentation
Zhiguo Lu, Jianwen Lou, Mingjun Ma, Hairong Jin, Youyi Zheng, Kun Zhou
Abstract
3D teeth segmentation, involving the localization of tooth instances and their semantic categorization in 3D dental models, is a critical yet challenging task in digital dentistry due to the complexity of real-world dentition. In this paper, we propose 3DTeethSAM, an adaptation of the Segment Anything Model 2 (SAM2) for 3D teeth segmentation. SAM2 is a pretrained foundation model for image and video segmentation, demonstrating a strong backbone in various downstream scenarios. To adapt SAM2 for 3D teeth data, we render images of 3D teeth models from predefined views, apply SAM2 for 2D segmentation, and reconstruct 3D results using 2D-3D projections. Since SAM2
BibTeX
@inproceedings{aaai2026_3dteethsamtaming,
title = {3DTeethSAM: Taming SAM2 for 3D Teeth Segmentation},
author = {Zhiguo Lu and Jianwen Lou and Mingjun Ma and Hairong Jin and Youyi Zheng and Kun Zhou},
booktitle = {AAAI 2026},
year = {2026}
}