# VBCD: A Voxel-Based Framework for Personalized Dental Crown Design

arXiv: https://arxiv.org/abs/2507.17205 · HTML: https://mlsanity.com/paper/2507.17205/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 9 of its 26 references that are in the map.
- First posted on arXiv 23 Jul 2025.

## Builds on (most cited first, 9 of 9)

- [4D Spatio-Temporal ConvNets: Minkowski Convolutional Neural Networks](https://mlsanity.com/paper/1904.08755/) (2019)
- [ECA-Net: Efficient Channel Attention for Deep Convolutional Neural Networks](https://mlsanity.com/paper/1910.03151/) (2019)
- [PCN: Point Completion Network](https://mlsanity.com/paper/1808.00671/) (2018)
- [Shape As Points: A Differentiable Poisson Solver](https://mlsanity.com/paper/2106.03452/) (2021)
- [GRNet: Gridding Residual Network for Dense Point Cloud Completion](https://mlsanity.com/paper/2006.03761/) (2020)
- [Neural Kernel Surface Reconstruction](https://mlsanity.com/paper/2305.19590/) (2023)
- [Comprehensive Review of Deep Learning-Based 3D Point Cloud Completion Processing and Analysis](https://mlsanity.com/paper/2203.03311/) (2022)
- [SenseCare: A Research Platform for Medical Image Informatics and Interactive 3D Visualization](https://mlsanity.com/paper/2004.07031/) (2020)
- [From Mesh Completion to AI Designed Crown](https://mlsanity.com/paper/2501.04914/) (2025)

## Built on by (most cited first, 2 of 2)

- [DM-CFO: A Diffusion Model for Compositional 3D Tooth Generation with Collision-Free Optimization](https://mlsanity.com/paper/2603.03602/) (2026)
- [Dissecting the Dental Lung Cancer Axis via Mendelian Randomization and Mediation Analysis](https://mlsanity.com/paper/2507.18287/) (2025)

## Abstract (arXiv)

The design of restorative dental crowns from intraoral scans is labor-intensive for dental technicians. To address this challenge, we propose a novel voxel-based framework for automated dental crown design (VBCD). The VBCD framework generates an initial coarse dental crown from voxelized intraoral scans, followed by a fine-grained refiner incorporating distance-aware supervision to improve accuracy and quality. During the training stage, we employ the Curvature and Margin line Penalty Loss (CMPL) to enhance the alignment of the generated crown with the margin line. Additionally, a positional prompt based on the FDI tooth numbering system is introduced to further improve the accuracy of the generated dental crowns. Evaluation on a large-scale dataset of intraoral scans demonstrated that our approach outperforms existing methods, providing a robust solution for personalized dental crown design.