# From Mesh Completion to AI Designed Crown

arXiv: https://arxiv.org/abs/2501.04914 · HTML: https://mlsanity.com/paper/2501.04914/

## Key facts

- Cited by 9 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 9 Jan 2025.

## Built on by (most cited first, 9 of 9)

- [ToothForge: Automatic Dental Shape Generation using Synchronized Spectral Embeddings](https://mlsanity.com/paper/2506.02702/) (2025)
- [VBCD: A Voxel-Based Framework for Personalized Dental Crown Design](https://mlsanity.com/paper/2507.17205/) (2025)
- [Multimodal Contrastive Pretraining of CBCT and IOS for Enhanced Tooth Segmentation](https://mlsanity.com/paper/2509.07923/) (2025)
- [MADCrowner: Margin Aware Dental Crown Design with Template Deformation and Refinement](https://mlsanity.com/paper/2603.04771/) (2026)
- [UniDCF: A Foundation Model for Comprehensive Dentocraniofacial Hard Tissue Reconstruction](https://mlsanity.com/paper/2508.11728/) (2025)
- [Text Condition Embedded Regression Network for Automated Dental Abutment Design](https://mlsanity.com/paper/2511.22578/) (2025)
- [Memory-Guided Point Cloud Completion for Dental Reconstruction](https://mlsanity.com/paper/2512.03598/) (2025)
- [From Full and Partial Intraoral Scans to Crown Proposal: A Classification-Guided Restoration Assistance Pipeline](https://mlsanity.com/paper/2605.15241/) (2026)
- [Deep Spectral Models for Robust Dental Shape Generation](https://mlsanity.com/paper/2606.31293/) (2026)

## Abstract (arXiv)

Designing a dental crown is a time-consuming and labor intensive process. Our goal is to simplify crown design and minimize the tediousness of making manual adjustments while still ensuring the highest level of accuracy and consistency. To this end, we present a new end- to-end deep learning approach, coined Dental Mesh Completion (DMC), to generate a crown mesh conditioned on a point cloud context. The dental context includes the tooth prepared to receive a crown and its surroundings, namely the two adjacent teeth and the three closest teeth in the opposing jaw. We formulate crown generation in terms of completing this point cloud context. A feature extractor first converts the input point cloud into a set of feature vectors that represent local regions in the point cloud. The set of feature vectors is then fed into a transformer to predict a new set of feature vectors for the missing region (crown). Subsequently, a point reconstruction head, followed by a multi-layer perceptron, is used to predict a dense set of points with normals. Finally, a differentiable point-to-mesh layer serves to reconstruct the crown surface mesh. We compare our DMC method to a graph-based convolutional neural network which learns to deform a crown mesh from a generic crown shape to the target geometry. Extensive experiments on our dataset demonstrate the effectiveness of our method, which attains an average of 0.062 Chamfer Distance.The code is available at:https://github.com/Golriz-code/DMC.gi