README.md

---
license: cc-by-4.0
language:
- en
pretty_name: MegaUnScene
size_categories:
- n<1K
tags:
- 3d
- image
configs:
- config_name: reconstructions
  data_files:
  - split: all
    path:
    - "reconstructions.csv"
  default: true
- config_name: images
  data_files:
  - split: all
    path:
    - "images.csv"
---

<div align="center">
<h1>Emergent Extreme-View Geometry in 3D Foundation Models</h1>

<a href="https://arxiv.org/abs/2511.22686"><img src="https://img.shields.io/badge/arXiv-2511.22686-b31b1b" alt="arXiv"></a> &nbsp; <a href="https://cornell-vailab.github.io/Ext-3DFMs/"><img src="https://img.shields.io/badge/Project_Page-green" alt="Project Page"></a>

**Yiwen Zhang**¹ &nbsp; **Joseph Tung**² &nbsp; **Ruojin Cai**³ &nbsp; **David Fouhey**² &nbsp; **Hadar Averbuch-Elor**¹

¹Cornell University &nbsp; ²New York University &nbsp; ³Kempner Institute, Harvard University
</div>

# MegaUnScene Benchmark
## Overview
**MegaUnScene** is a dataset of Internet scenes unseen by existing 3DFMs for benchmarking. There are three test splits split across two evaluation tasks:
- **Relative Pose Estimation:** UnScenePairs and UnScenePairs-t
- **Dense 3D Reconstruction:** UnSceneRecon

## Benchmarking Relative Pose in the Wild
**UnScenePairs** targets image pairs with predominant rotational motion, while **UnScenePairs-t** focuses on pairs with larger camera baselines. Unlike prior benchmarks, these subsets capture unconstrained, in-the-wild views unseen by 3DFMs. In total, they comprise over 6,000 image pairs across more than 450 scenes, including substantial non-overlapping splits.

## UnSceneRecon: Benchmarking Dense Reconstruction in the Wild
**UnSceneRecon** is a subset comprising 100 in-the-wild reconstructions with metric scale annotations. This benchmark evaluates dense reconstruction quality on unconstrained Internet photos exhibiting diverse lighting conditions, transients, and varying camera models.

---

# Dataset Download
Please refer to https://huggingface.co/docs/hub/en/datasets-downloading on how to download datasets from HuggingFace. 

We provide a [post-processing script](https://github.com/theREALevan/extreme-view-3dfm/blob/main/tools/unzip_and_decompress_megaunscene.py) to prepare the dataset. In the direct download, depth maps are compressed with bit shuffling to minimize download size. Each scenes' image and depth map folders are zipped. This script unzips all folders and decompresses all depth maps in a given base directory.

Example use: `python unzip_and_decompress_megaunscene.py --megaunscene_base {PATH_TO_MEGAUNSCENE_ROOT}`

Note: Python library [numcodecs](https://numcodecs.readthedocs.io/en/stable/#module-numcodecs) is required to decompress depth maps; it is installable with `pip install numcodecs`.


## Dataset Size
The full compressed dataset download is ~202G, or ~706G uncompressed.

---

# Dataset Structure
```
megaunscene/
├── images.csv                          # Metadata for all images
├── reconstructions.csv                 # Metadata for all reconstructions
└── scenes/                             # Main scene data directory
    ├── {scene_name}/                   # Scene name
    │   ├── {recon_id}/                 # Reconstruction ID
    │   │   ├── images/
    │   │   │   └── ...
    │   │   ├── depth_maps/
    │   │   │   └── ...
    │   │   └── sparse/                 # Sparse reconstruction data (COLMAP format)
    │   │       ├── cameras.bin
    │   │       ├── images.bin
    │   │       ├── points3D.bin
    │   │       └── ...
    │   └── ... (additional reconstructions)
    └── ... (469 scenes total)
```

# Metadata

## reconstructions.csv

[reconstructions.csv](https://huggingface.co/datasets/cornell-vailab/megaunscene/blob/main/reconstructions.csv) contains metadata for all reconstructions in the dataset.

| Column | Type | Description |
|--------|------|-------------|
| `scene` | string | Scene name (e.g., "Predjama_Castle") |
| `recon_id` | integer | Reconstruction ID for the scene (0, 1, 2, ...) |
| `in_unscene_recon` | boolean | Whether this reconstruction is in UnSceneRecon |
| `in_unscene_pairs` | boolean | Whether this reconstruction is in UnScenePairs |
| `in_unscene_pairs_t` | boolean | Whether this reconstruction is in UnScenePairs-t |

## images.csv

[images.csv](https://huggingface.co/datasets/cornell-vailab/megaunscene/blob/main/images.csv) contains metadata for all images in the dataset, including licensing information sourced from Wikimedia Commons.

| Column | Type | Description |
|--------|------|-------------|
| `scene` | string | Scene name (e.g., "Predjama_Castle") |
| `recon_id` | integer | Reconstruction ID (0, 1, 2, ...) |
| `image_id` | integer | Unique image identifier within the reconstruction; matches COLMAP's images.bin image ID. |
| `image_name` | string | Relative path to image from subcategory directory (e.g., "commons/Views_of_Predjama_Castle/0/pictures/Stronghold-2711853.jpg") as taken from COLMAP's iamges.bin |
| `subcategory` | string | Parsed Wikimedia Commons subcategory name (e.g., "Views_of_Predjama_Castle") |
| `image_filename` | string | Parsed image filename (e.g., "Stronghold-2711853.jpg") |
| `has_depth` | boolean | Whether a depth map exists for this image |
| `conflicts_with_megascenes` | boolean | Whether this image conflicts with MegaScenes dataset |
| `credit` | string | Image credit/attribution (HTML formatted) |
| `artist` | string | Artist/photographer name (HTML formatted) |
| `license` | string | Full license identifier |
| `license_short_name` | string | Short license name (e.g., "CC BY-SA 4.0") |
| `license_url` | string | URL to license text |
| `usage_terms` | string | Human-readable license description |
| `user` | string | Wikimedia Commons uploader username |

## Notes

- **Scene names**: URL-encoded (spaces → underscores, slashes → `%2F`)
- **Image paths**: Relative to the directory in the `images/` folder
- **File locations**: 
  - Images: `scenes/{scene}/{recon_id}/images/{image_name}`, where `image_name` is full relative path defined in `images.csv`
  - Depth maps: `scenes/{scene}/{recon_id}/depth_maps/{image_name}.npy` (if `has_depth=true`)
  - Sparse reconstruction: `scenes/{scene}/{recon_id}/sparse/`


---

# Citation

If you found this dataset helpful, please cite

```
@misc{zhang2025emergentextremeviewgeometry3d,
      title={Emergent Extreme-View Geometry in 3D Foundation Models}, 
      author={Yiwen Zhang and Joseph Tung and Ruojin Cai and David Fouhey and Hadar Averbuch-Elor},
      year={2025},
      eprint={2511.22686},
      archivePrefix={arXiv},
      primaryClass={cs.CV},
      url={https://arxiv.org/abs/2511.22686}, 
}
```

# License

This dataset is licensed under the Creative Commons Attribution 4.0 International License. The photos for each scene are sourced from Wikimedia Commons and have their own licenses; please see images.csv for additional details.