mlsanityData to 7 Oct 2026

Cultural Heritage 3D Reconstruction with Diffusion Networks

arXiv 2410.10927 ↗First posted 14 Oct 2024cs.CV cs.AI cs.GRcited by 3

On the map: Diffusion models → Image generation, text-to-image and editing

Abstract (arXiv)

This article explores the use of recent generative AI algorithms for repairing cultural heritage objects, leveraging a conditional diffusion model designed to reconstruct 3D point clouds effectively. Our study evaluates the model's performance across general and cultural heritage-specific settings. Results indicate that, with considerations for object variability, the diffusion model can accurately reproduce cultural heritage geometries. Despite encountering challenges like data diversity and outlier sensitivity, the model demonstrates significant potential in artifact restoration research. This work lays groundwork for advancing restoration methodologies for ancient artifacts using AI technologies.

Key facts. Cited by 3 papers in the map (data through 7 Oct 2026). Placed in Image generation, text-to-image and editing, one of 14 directions in Diffusion models. Builds on 13 of its 25 references that are in the map. First posted on arXiv 14 Oct 2024.

Builds on

13 of 25 references.

References · 13

  1. 2017PointNet++: Deep Hierarchical Feature Learning on Point Sets in a Metric Spacecited by 4,384
  2. 2015ShapeNet: An Information-Rich 3D Model Repositorycited by 2,880
  3. 2019DeepSDF: Learning Continuous Signed Distance Functions for Shape Representationcited by 2,682
  4. 20213D Shape Generation and Completion through Point-Voxel Diffusioncited by 516
  5. 2019Point-Voxel CNN for Efficient 3D Deep Learningcited by 343
  6. 2022SDFusion: Multimodal 3D Shape Completion, Reconstruction, and Generationcited by 287
  7. 2021A Conditional Point Diffusion-Refinement Paradigm for 3D Point Cloud Completioncited by 100
  8. 2023Point-Cloud Completion with Pretrained Text-to-image Diffusion Modelscited by 37
  9. 2023DiffComplete: Diffusion-based Generative 3D Shape Completioncited by 35
  10. 2018Development and Validation of Deep Learning Algorithms for Detection of Critical Findings in Head CT Scanscited by 24
  11. 2023Point Cloud Diffusion Models for Automatic Implant Generationcited by 19
  12. 20173D Reconstruction of Incomplete Archaeological Objects Using a Generative Adversarial Networkcited by 12
1 more
  1. 2022DeepMend: Learning Occupancy Functions to Represent Shape for Repaircited by 6

Built on by

Cited by year

Citing papers per year: 2025: 3.

3 papers.

  1. 2025Exploring Typographic Visual Prompts Injection Threats in Cross-Modality Generation Modelscited by 12
  2. 2025VaseVQA-3D: Benchmarking 3D VLMs on Ancient Greek Potterycited by 4
  3. 2025Bridging Text and Video Generation: A Surveycited by 2

Similar, not cited

Close in wording, no citation either way

  1. 2025AI-assisted 3D Preservation and Reconstruction of Temple Arts ↗
  2. 2025DiffuMural: Restoring Dunhuang Murals with Multi-scale Diffusion
  3. 2025Oitijjo-3D: Generative AI Framework for Rapid 3D Heritage Reconstruction from Street View Imagery
  4. 2024From Pampas to Pixels: Fine-Tuning Diffusion Models for Ga'ucho Heritage
  5. 2023Diffusion Based Augmentation for Captioning and Retrieval in Cultural Heritage
  6. 2023Knowledge-Aware Artifact Image Synthesis with LLM-Enhanced Prompting and Multi-Source Supervision
  7. 2026Synthetic Data Augmentation for Multi-Task Chinese Porcelain Classification: A Stable Diffusion Approach
  8. 2026Mask-Conditioned Voxel Diffusion for Joint Geometry and Color Inpainting