# Dunhuang Grottoes Painting Dataset and Benchmark

arXiv: https://arxiv.org/abs/1907.04589 · HTML: https://mlsanity.com/paper/1907.04589/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 10 Jul 2019.

## Built on by (most cited first, 5 of 5)

- [MISF: Multi-level Interactive Siamese Filtering for High-Fidelity Image Inpainting](https://mlsanity.com/paper/2203.06304/) (2022)
- [HINT: High-quality INPainting Transformer with Mask-Aware Encoding and Enhanced Attention](https://mlsanity.com/paper/2402.14185/) (2024)
- [Towards Cross-modal Retrieval in Chinese Cultural Heritage Documents: Dataset and Solution](https://mlsanity.com/paper/2505.10921/) (2025)
- [CMAMRNet: A Contextual Mask-Aware Network Enhancing Mural Restoration Through Comprehensive Mask Guidance](https://mlsanity.com/paper/2508.07140/) (2025)
- [ARIN: Adaptive Resampling and Instance Normalization for Robust Blind Inpainting of Dunhuang Cave Paintings](https://mlsanity.com/paper/2402.16188/) (2024)

## Abstract (arXiv)

This document introduces the background and the usage of the Dunhuang Grottoes Dataset and the benchmark. The documentation first starts with the background of the Dunhuang Grotto, which is widely recognised as an priceless heritage. Given that digital method is the modern trend for heritage protection and restoration. Follow the trend, we release the first public dataset for Dunhuang Grotto Painting restoration. The rest of the documentation details the painting data generation. To enable a data driven fashion, this dataset provided a large number of training and testing example which is sufficient for a deep learning approach. The detailed usage of the dataset as well as the benchmark is described.