mlsanityData to 7 Oct 2026

Point Cloud Diffusion Models for Automatic Implant Generation

arXiv 2303.08061 ↗First posted 14 Mar 2023eess.IV cs.CVcited by 19

On the map: Diffusion models → 3D, point cloud and scene generation

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.

Key facts. Cited by 19 papers in the map (data through 7 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.

Builds on

18 of 36 references.

Closest ancestors · 5

Same direction, newest first

  1. 2022Point-E: A System for Generating 3D Point Clouds from Complex Prompts16 Dec
  2. 2022LION: Latent Point Diffusion Models for 3D Shape Generation12 Oct
  3. 2022RePaint: Inpainting using Denoising Diffusion Probabilistic Models24 Jan
  4. 2021A Conditional Point Diffusion-Refinement Paradigm for 3D Point Cloud Completion7 Dec
  5. 2021Diffusion Probabilistic Models for 3D Point Cloud Generation2 Mar

Other references · 13

  1. 2020Denoising Diffusion Probabilistic Modelscited by 20,851
  2. 2020Denoising Diffusion Implicit Modelscited by 9,655
  3. 2015Deep Unsupervised Learning using Nonequilibrium Thermodynamicscited by 7,005
  4. 2017PointNet++: Deep Hierarchical Feature Learning on Point Sets in a Metric Spacecited by 4,384
  5. 2022Elucidating the Design Space of Diffusion-Based Generative Modelscited by 2,994
  6. 2021Palette: Image-to-Image Diffusion Modelscited by 1,296
  7. 2020Implicit Geometric Regularization for Learning Shapescited by 707
  8. 20213D Shape Generation and Completion through Point-Voxel Diffusioncited by 516
  9. 2019Point-Voxel CNN for Efficient 3D Deep Learningcited by 343
  10. 2021Diffusion Models for Implicit Image Segmentation Ensemblescited by 198
  11. 2021Shape As Points: A Differentiable Poisson Solvercited by 186
  12. 2020Point2Mesh: A Self-Prior for Deformable Meshescited by 41
1 more
  1. 2020Cranial Implant Design via Virtual Craniectomy with Shape Priorscited by 5

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Cited by year (2026 to 7 Oct)

Citing papers per year: 2023: 3; 2024: 9; 2025: 4; 2026: 3.

19 papers.

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  1. 2024WDM: 3D Wavelet Diffusion Models for High-Resolution Medical Image Synthesiscited by 56
  2. 2023RenAIssance: A Survey into AI Text-to-Image Generation in the Era of Large Modelcited by 47
  3. 2023MedShapeNet -- A Large-Scale Dataset of 3D Medical Shapes for Computer Visioncited by 31
  4. 2024Denoising Diffusion Models for 3D Healthy Brain Tissue Inpaintingcited by 22
  5. 2024Point2SSM++: Self-Supervised Learning of Anatomical Shape Models from Point Cloudscited by 5
  6. 2024SCorP: Statistics-Informed Dense Correspondence Prediction Directly from Unsegmented Medical Imagescited by 3
  7. 2024Cultural Heritage 3D Reconstruction with Diffusion Networkscited by 3
  8. 2024AI-Enhanced Virtual Reality in Medicine: A Comprehensive Surveycited by 2
  9. 2024Automatic Cranial Defect Reconstruction with Self-Supervised Deep Deformable Masked Autoencoderscited by 2
  10. 2024Improving Deep Learning-based Automatic Cranial Defect Reconstruction by Heavy Data Augmentation: From Image Registration to Latent Diffusion Modelscited by 2
  11. 2025A Continuous-Time Consistency Model for 3D Point Cloud Generationcited by 2
  12. 2023Large Intestine 3D Shape Refinement Using Point Diffusion Models for Digital Phantom Generationcited by 1
7 more
  1. 2026NOIR: Neural Operator mapping for Implicit Representationscited by 1
  2. 2026MedPCFM: Improving Medical Point Cloud Completion by Integrating Point Transformers and Flow Matchingcited by 1
  3. 2024Automatic Skull Reconstruction by Deep Learnable Symmetry Enforcementcited by 0
  4. 20253D Shape Generation: A Surveycited by 0
  5. 2025UniDCF: A Foundation Model for Comprehensive Dentocraniofacial Hard Tissue Reconstructioncited by 0
  6. 2025InJecteD: Analyzing Trajectories and Drift Dynamics in Denoising Diffusion Probabilistic Models for 2D Point Cloud Generationcited by 0
  7. 2026MedPCFM-TED: One-Step Point Cloud Flow Matching for Implant Generation via Teacher-Guided Endpoint Distillationcited by 0

Similar, not cited

Close in wording, no citation either way

  1. 2022Medical Diffusion: Denoising Diffusion Probabilistic Models for 3D Medical Image Generation
  2. 2022Back to the Roots: Reconstructing Large and Complex Cranial Defects using an Image-based Statistical Shape Model
  3. 2024Deformable 3D Shape Diffusion Model
  4. 20243D MedDiffusion: A 3D Medical Latent Diffusion Model for Controllable and High-quality Medical Image Generation
  5. 2026DVD: Discrete Voxel Diffusion for 3D Generation and Editing
  6. 2025PartDiffuser: Part-wise 3D Mesh Generation via Discrete Diffusion
  7. 2024Efficient and Scalable Point Cloud Generation with Sparse Point-Voxel Diffusion Models
  8. 2025Scaffold Diffusion: Sparse Multi-Category Voxel Structure Generation with Discrete Diffusion