# Point Cloud Diffusion Models for Automatic Implant Generation

arXiv: https://arxiv.org/abs/2303.08061 · HTML: https://mlsanity.com/paper/2303.08061/

## Key facts

- Cited by 19 papers in the map (data through 14 Oct 2026).
- Placed in 3D, point cloud and scene generation, one of 14 directions in Diffusion models.
- Builds on 18 of its 36 references that are in the map.
- First posted on arXiv 14 Mar 2023.

## Placed in

- Diffusion models / [3D, point cloud and scene generation](https://mlsanity.com/diffusion/3d-point-cloud-and-scene-generation/)

## Builds on (most cited first, 18 of 18)

- [Denoising Diffusion Probabilistic Models](https://mlsanity.com/paper/2006.11239/) (2020)
- [Denoising Diffusion Implicit Models](https://mlsanity.com/paper/2010.02502/) (2020)
- [Deep Unsupervised Learning using Nonequilibrium Thermodynamics](https://mlsanity.com/paper/1503.03585/) (2015)
- [PointNet++: Deep Hierarchical Feature Learning on Point Sets in a Metric Space](https://mlsanity.com/paper/1706.02413/) (2017)
- [Elucidating the Design Space of Diffusion-Based Generative Models](https://mlsanity.com/paper/2206.00364/) (2022)
- [RePaint: Inpainting using Denoising Diffusion Probabilistic Models](https://mlsanity.com/paper/2201.09865/) (2022)
- [Palette: Image-to-Image Diffusion Models](https://mlsanity.com/paper/2111.05826/) (2021)
- [Diffusion Probabilistic Models for 3D Point Cloud Generation](https://mlsanity.com/paper/2103.01458/) (2021)
- [Implicit Geometric Regularization for Learning Shapes](https://mlsanity.com/paper/2002.10099/) (2020)
- [Point-E: A System for Generating 3D Point Clouds from Complex Prompts](https://mlsanity.com/paper/2212.08751/) (2022)
- [LION: Latent Point Diffusion Models for 3D Shape Generation](https://mlsanity.com/paper/2210.06978/) (2022)
- [3D Shape Generation and Completion through Point-Voxel Diffusion](https://mlsanity.com/paper/2104.03670/) (2021)
- [Point-Voxel CNN for Efficient 3D Deep Learning](https://mlsanity.com/paper/1907.03739/) (2019)
- [Diffusion Models for Implicit Image Segmentation Ensembles](https://mlsanity.com/paper/2112.03145/) (2021)
- [Shape As Points: A Differentiable Poisson Solver](https://mlsanity.com/paper/2106.03452/) (2021)
- [A Conditional Point Diffusion-Refinement Paradigm for 3D Point Cloud Completion](https://mlsanity.com/paper/2112.03530/) (2021)
- [Point2Mesh: A Self-Prior for Deformable Meshes](https://mlsanity.com/paper/2005.11084/) (2020)
- [Cranial Implant Design via Virtual Craniectomy with Shape Priors](https://mlsanity.com/paper/2009.13704/) (2020)

## Built on by (most cited first, 19 of 19)

- [WDM: 3D Wavelet Diffusion Models for High-Resolution Medical Image Synthesis](https://mlsanity.com/paper/2402.19043/) (2024)
- [RenAIssance: A Survey into AI Text-to-Image Generation in the Era of Large Model](https://mlsanity.com/paper/2309.00810/) (2023)
- [MedShapeNet -- A Large-Scale Dataset of 3D Medical Shapes for Computer Vision](https://mlsanity.com/paper/2308.16139/) (2023)
- [Denoising Diffusion Models for 3D Healthy Brain Tissue Inpainting](https://mlsanity.com/paper/2403.14499/) (2024)
- [Point2SSM++: Self-Supervised Learning of Anatomical Shape Models from Point Clouds](https://mlsanity.com/paper/2405.09707/) (2024)
- [Cultural Heritage 3D Reconstruction with Diffusion Networks](https://mlsanity.com/paper/2410.10927/) (2024)
- [SCorP: Statistics-Informed Dense Correspondence Prediction Directly from Unsegmented Medical Images](https://mlsanity.com/paper/2404.17967/) (2024)
- [AI-Enhanced Virtual Reality in Medicine: A Comprehensive Survey](https://mlsanity.com/paper/2402.03093/) (2024)
- [Automatic Cranial Defect Reconstruction with Self-Supervised Deep Deformable Masked Autoencoders](https://mlsanity.com/paper/2404.13106/) (2024)
- [Improving Deep Learning-based Automatic Cranial Defect Reconstruction by Heavy Data Augmentation: From Image Registration to Latent Diffusion Models](https://mlsanity.com/paper/2406.06372/) (2024)
- [A Continuous-Time Consistency Model for 3D Point Cloud Generation](https://mlsanity.com/paper/2509.01492/) (2025)
- [Large Intestine 3D Shape Refinement Using Point Diffusion Models for Digital Phantom Generation](https://mlsanity.com/paper/2309.08289/) (2023)
- [NOIR: Neural Operator mapping for Implicit Representations](https://mlsanity.com/paper/2603.13118/) (2026)
- [MedPCFM: Improving Medical Point Cloud Completion by Integrating Point Transformers and Flow Matching](https://mlsanity.com/paper/2606.24433/) (2026)
- [Automatic Skull Reconstruction by Deep Learnable Symmetry Enforcement](https://mlsanity.com/paper/2411.17342/) (2024)
- [3D Shape Generation: A Survey](https://mlsanity.com/paper/2506.22678/) (2025)
- [UniDCF: A Foundation Model for Comprehensive Dentocraniofacial Hard Tissue Reconstruction](https://mlsanity.com/paper/2508.11728/) (2025)
- [InJecteD: Analyzing Trajectories and Drift Dynamics in Denoising Diffusion Probabilistic Models for 2D Point Cloud Generation](https://mlsanity.com/paper/2509.12239/) (2025)
- [MedPCFM-TED: One-Step Point Cloud Flow Matching for Implant Generation via Teacher-Guided Endpoint Distillation](https://mlsanity.com/paper/2609.16934/) (2026)

## Abstract (arXiv)

Advances in 3D printing of biocompatible materials make patient-specific implants increasingly popular. The design of these implants is, however, still a tedious and largely manual process. Existing approaches to automate implant generation are mainly based on 3D U-Net architectures on downsampled or patch-wise data, which can result in a loss of detail or contextual information. Following the recent success of Diffusion Probabilistic Models, we propose a novel approach for implant generation based on a combination of 3D point cloud diffusion models and voxelization networks. Due to the stochastic sampling process in our diffusion model, we can propose an ensemble of different implants per defect, from which the physicians can choose the most suitable one. We evaluate our method on the SkullBreak and SkullFix datasets, generating high-quality implants and achieving competitive evaluation scores.