# Cultural Heritage 3D Reconstruction with Diffusion Networks

arXiv: https://arxiv.org/abs/2410.10927 · HTML: https://mlsanity.com/paper/2410.10927/

## Key facts

- Cited by 4 papers in the map (data through 14 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.

## Placed in

- Diffusion models / [Image generation, text-to-image and editing](https://mlsanity.com/diffusion/image-generation-text-to-image-and-editing/)

## Builds on (most cited first, 13 of 13)

- [PointNet++: Deep Hierarchical Feature Learning on Point Sets in a Metric Space](https://mlsanity.com/paper/1706.02413/) (2017)
- [ShapeNet: An Information-Rich 3D Model Repository](https://mlsanity.com/paper/1512.03012/) (2015)
- [DeepSDF: Learning Continuous Signed Distance Functions for Shape Representation](https://mlsanity.com/paper/1901.05103/) (2019)
- [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)
- [SDFusion: Multimodal 3D Shape Completion, Reconstruction, and Generation](https://mlsanity.com/paper/2212.04493/) (2022)
- [A Conditional Point Diffusion-Refinement Paradigm for 3D Point Cloud Completion](https://mlsanity.com/paper/2112.03530/) (2021)
- [Point-Cloud Completion with Pretrained Text-to-image Diffusion Models](https://mlsanity.com/paper/2306.10533/) (2023)
- [DiffComplete: Diffusion-based Generative 3D Shape Completion](https://mlsanity.com/paper/2306.16329/) (2023)
- [Development and Validation of Deep Learning Algorithms for Detection of Critical Findings in Head CT Scans](https://mlsanity.com/paper/1803.05854/) (2018)
- [Point Cloud Diffusion Models for Automatic Implant Generation](https://mlsanity.com/paper/2303.08061/) (2023)
- [3D Reconstruction of Incomplete Archaeological Objects Using a Generative Adversarial Network](https://mlsanity.com/paper/1711.06363/) (2017)
- [DeepMend: Learning Occupancy Functions to Represent Shape for Repair](https://mlsanity.com/paper/2210.05728/) (2022)

## Built on by (most cited first, 4 of 4)

- [Exploring Typographic Visual Prompts Injection Threats in Cross-Modality Generation Models](https://mlsanity.com/paper/2503.11519/) (2025)
- [VaseVQA-3D: Benchmarking 3D VLMs on Ancient Greek Pottery](https://mlsanity.com/paper/2510.04479/) (2025)
- [Bridging Text and Video Generation: A Survey](https://mlsanity.com/paper/2510.04999/) (2025)
- [SHReg: Strictly Rotation-Equivariant Point Cloud Registration via Spherical Harmonics](https://mlsanity.com/paper/2607.23096/) (2026)

## 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.