# Text-Conditioned Multi-Expert Regression Framework for Fully Automated Multi-Abutment Design

arXiv: https://arxiv.org/abs/2604.09047 · HTML: https://mlsanity.com/paper/2604.09047/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 15 of its 37 references that are in the map.
- First posted on arXiv 10 Apr 2026.

## Builds on (most cited first, 15 of 15)

- [PointNet: Deep Learning on Point Sets for 3D Classification and Segmentation](https://mlsanity.com/paper/1612.00593/) (2016)
- [PointNet++: Deep Hierarchical Feature Learning on Point Sets in a Metric Space](https://mlsanity.com/paper/1706.02413/) (2017)
- [Masked Autoencoders for Point Cloud Self-supervised Learning](https://mlsanity.com/paper/2203.06604/) (2022)
- [PointMamba: A Simple State Space Model for Point Cloud Analysis](https://mlsanity.com/paper/2402.10739/) (2024)
- [Subdivision-Based Mesh Convolution Networks](https://mlsanity.com/paper/2106.02285/) (2021)
- [Towards Compact 3D Representations via Point Feature Enhancement Masked Autoencoders](https://mlsanity.com/paper/2312.10726/) (2023)
- [MeshMAE: Masked Autoencoders for 3D Mesh Data Analysis](https://mlsanity.com/paper/2207.10228/) (2022)
- [Two-Stage Mesh Deep Learning for Automated Tooth Segmentation and Landmark Localization on 3D Intraoral Scans](https://mlsanity.com/paper/2109.11941/) (2021)
- [A fully automated method for 3D individual tooth identification and segmentation in dental CBCT](https://mlsanity.com/paper/2102.06060/) (2021)
- [DArch: Dental Arch Prior-assisted 3D Tooth Instance Segmentation](https://mlsanity.com/paper/2204.11911/) (2022)
- [Teeth3DS+: An Extended Benchmark for Intraoral 3D Scans Analysis](https://mlsanity.com/paper/2210.06094/) (2022)
- [ImplantFormer: Vision Transformer based Implant Position Regression Using Dental CBCT Data](https://mlsanity.com/paper/2210.16467/) (2022)
- [Two-Stream Regression Network for Dental Implant Position Prediction](https://mlsanity.com/paper/2305.10044/) (2023)
- [3D Dental Model Segmentation with Geometrical Boundary Preserving](https://mlsanity.com/paper/2503.23702/) (2025)
- [SSA3D: Text-Conditioned Assisted Self-Supervised Framework for Automatic Dental Abutment Design](https://mlsanity.com/paper/2512.11507/) (2025)

## Abstract (arXiv)

Dental implant abutments serve as the geometric and biomechanical interface between the implant fixture and the prosthetic crown, yet their design relies heavily on manual effort and is time-consuming. Although deep neural networks have been proposed to assist dentists in designing abutments, most existing approaches remain largely manual or semi-automated, requiring substantial clinician intervention and lacking scalability in multi-abutment scenarios. To address these limitations, we propose TEMAD, a fully automated, text-conditioned multi-expert architecture for multi-abutment design. This framework integrates implant site localization and implant system, compatible abutment parameter regression into a unified pipeline. Specifically, we introduce an Implant Site Identification Network (ISIN) to automatically localize implant sites and provide this information to the subsequent multi-abutment regression network. We further design a Tooth-Conditioned Feature-wise Linear Modulation (TC-FiLM) module, which adaptively calibrates mesh representations using tooth embeddings to enable position-specific feature modulation. Additionally, a System-Prompted Mixture-of-Experts (SPMoE) mechanism leverages implant system prompts to guide expert selection, ensuring system-aware regression. Extensive experiments on a large-scale abutment design dataset show that TEMAD achieves state-of-the-art performance compared to existing methods, particularly in multi-abutment settings, validating its effectiveness for fully automated dental implant planning.