# X2Teeth: 3D Teeth Reconstruction from a Single Panoramic Radiograph

arXiv: https://arxiv.org/abs/2108.13004 · HTML: https://mlsanity.com/paper/2108.13004/

## Key facts

- Cited by 12 papers in the map (data through 14 Oct 2026).
- Builds on 5 of its 16 references that are in the map.
- First posted on arXiv 30 Aug 2021.

## Builds on (most cited first, 5 of 5)

- [ShapeNet: An Information-Rich 3D Model Repository](https://mlsanity.com/paper/1512.03012/) (2015)
- [3D-R2N2: A Unified Approach for Single and Multi-view 3D Object Reconstruction](https://mlsanity.com/paper/1604.00449/) (2016)
- [What Do Single-view 3D Reconstruction Networks Learn?](https://mlsanity.com/paper/1905.03678/) (2019)
- [Pix3D: Dataset and Methods for Single-Image 3D Shape Modeling](https://mlsanity.com/paper/1804.04610/) (2018)
- [Octree Generating Networks: Efficient Convolutional Architectures for High-resolution 3D Outputs](https://mlsanity.com/paper/1703.09438/) (2017)

## Built on by (most cited first, 12 of 12)

- [3DPX: Progressive 2D-to-3D Oral Image Reconstruction with Hybrid MLP-CNN Networks](https://mlsanity.com/paper/2408.01292/) (2024)
- [3D Teeth Reconstruction from Panoramic Radiographs using Neural Implicit Functions](https://mlsanity.com/paper/2311.16524/) (2023)
- [PX2Tooth: Reconstructing the 3D Point Cloud Teeth from a Single Panoramic X-ray](https://mlsanity.com/paper/2411.03725/) (2024)
- [ToothInpaintor: Tooth Inpainting from Partial 3D Dental Model and 2D Panoramic Image](https://mlsanity.com/paper/2211.15502/) (2022)
- [Oral-3Dv2: 3D Oral Reconstruction from Panoramic X-Ray Imaging with Implicit Neural Representation](https://mlsanity.com/paper/2303.12123/) (2023)
- [NeBLa: Neural Beer-Lambert for 3D Reconstruction of Oral Structures from Panoramic Radiographs](https://mlsanity.com/paper/2304.04027/) (2023)
- [ViT-NeBLa: A Hybrid Vision Transformer and Neural Beer-Lambert Framework for Single-View 3D Reconstruction of Oral Anatomy from Panoramic Radiographs](https://mlsanity.com/paper/2506.13195/) (2025)
- [3DPX: Single Panoramic X-ray Analysis Guided by 3D Oral Structure Reconstruction](https://mlsanity.com/paper/2409.18701/) (2024)
- [X2BR: High-Fidelity 3D Bone Reconstruction from a Planar X-Ray Image with Hybrid Neural Implicit Methods](https://mlsanity.com/paper/2504.08675/) (2025)
- [HyDAR-Pano3D: A Hybrid Disentangled Anatomical Recovery Framework for Panoramic-to-3D Reconstruction](https://mlsanity.com/paper/2605.20827/) (2026)
- [K-U-KAN: Koopman-Enhanced U-KAN for 3D Dental Reconstruction from a Single Panoramic X-ray Radiograph](https://mlsanity.com/paper/2605.25163/) (2026)
- [X-Splat: Gaussian Splatting for 3D CBCT Generation from Single Panoramic Radiograph](https://mlsanity.com/paper/2607.02099/) (2026)

## Abstract (arXiv)

3D teeth reconstruction from X-ray is important for dental diagnosis and many clinical operations. However, no existing work has explored the reconstruction of teeth for a whole cavity from a single panoramic radiograph. Different from single object reconstruction from photos, this task has the unique challenge of constructing multiple objects at high resolutions. To conquer this task, we develop a novel ConvNet X2Teeth that decomposes the task into teeth localization and single-shape estimation. We also introduce a patch-based training strategy, such that X2Teeth can be end-to-end trained for optimal performance. Extensive experiments show that our method can successfully estimate the 3D structure of the cavity and reflect the details for each tooth. Moreover, X2Teeth achieves a reconstruction IoU of 0.681, which significantly outperforms the encoder-decoder method by 1.71X and the retrieval-based method by 1.52X. Our method can also be promising for other multi-anatomy 3D reconstruction tasks.