VBCD: A Voxel-Based Framework for Personalized Dental Crown Design
This work addresses the problem of automating personalized dental crown design for dental technicians, representing an incremental improvement with specific gains in accuracy.
The paper tackles the labor-intensive design of dental crowns from intraoral scans by proposing a voxel-based framework (VBCD) that generates and refines crowns with distance-aware supervision and a positional prompt, achieving superior performance over existing methods on a large-scale dataset.
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.