---
license: cc-by-4.0
task_categories:
- depth-estimation
language:
- en
tags:
- monocular-depth-estimation-evaluation
pretty_name: D2P
size_categories:
- n<1K
dataset_info:
  features:
  - name: image
    dtype: image
  - name: scene
    dtype: string
  - name: category
    dtype: string
  - name: image_name
    dtype: string
  - name: camera_model
    dtype: string
  - name: width
    dtype: int64
  - name: height
    dtype: int64
  - name: fx
    dtype: float64
  - name: fy
    dtype: float64
  - name: cx
    dtype: float64
  - name: cy
    dtype: float64
  - name: qw
    dtype: float64
  - name: qx
    dtype: float64
  - name: qy
    dtype: float64
  - name: qz
    dtype: float64
  - name: tx
    dtype: float64
  - name: ty
    dtype: float64
  - name: tz
    dtype: float64
  splits:
  - name: evaluation
    num_bytes: 6021182012
    num_examples: 1953
  download_size: 6953642145
  dataset_size: 6021182012
configs:
- config_name: default
  data_files:
  - split: evaluation
    path: data/evaluation-*
---
---
# The D2P dataset

The **D2P dataset** is a dataset based on the Depth2Pose monocular depth estimation benchmark, a pose-based evaluation of MDEs without ground-truth depth. The dataset contains challenging scenes beyond the distribution of common training data, together with a simple and extensible evaluation framework, presented on the github page. The scenes are divided into two categories: statues and vegetation. Undistorted images and reconstructions in standard colmap format is provided for each scene, together with a list of image pairs used for the evaluation.

[**paper** (coming later)]()  |  [**github**](https://github.com/kocurvik/depth2pose)  |  [**webpage**](https://kocurvik.github.io/depth2pose/) 

## Dataset Structure


```
d2p_dataset                           
├── statues/                           
│   ├── scene1/                          
│   │   ├── images/                       
│   │   │   ├── img1.png
│   │   │   ├── img2.png   
│   │   │   └── ... 
│   │   ├── sparse/
│   │   │   ├── cameras.txt
│   │   │   ├── frames.txt
│   │   │   ├── images.txt
│   │   │   ├── points3D.txt
│   │   │   └── rigs.txt
│   │   ├── scene1_image_list.txt
│   │   └── scene1_image_pairs.txt
│   ├── scene2/ 
│   │   └── ... 
│   └── ... 
└──  vegetation/
```


## Dataset Fields

Each **scene** contains:
- `images/`: RGB images
- `sparse/`: COLMAP reconstruction files:
  - camera parameters
  - frames
  - image poses
  - 3D points
  - rigs
- `scene1_image_list.txt`: List of all images used for the benchmark, found in the images/ folder 
- `scene1_image_pairs.txt`: List of all image pairs used for the benchmark, for which realtive pose is evaluated

### Direct Use

Benchmarking monocular depth estimators. For the current leaderboard, see the [Depth2Pose webpage](https://kocurvik.github.io/depth2pose/)

### Load with 🤗 Datasets

```python
from datasets import load_dataset

ds = load_dataset("floodgab/d2p_dataset")
print(ds["validation"][0])
```

### Loading Example

To download the Depth2Pose dataset

```python
from huggingface_hub import snapshot_download

path = snapshot_download("floodgab/d2p_dataset")
```

## Citation
If you use Depth2Pose in your research or find our work helpful, please cite
```bibtex
@misc{depth2pose,
  title={{Depth2Pose}: A Pose-Based Benchmark for Monocular Depth Estimation without Ground-Truth Depth},
  author={Kocur, Viktor and Aung, Sithu and Flood, Gabrielle and Ding, Yaqing and Bujnak, Lukas and Sattler, Torsten and Kukelova, Zuzana},
  year={2026},
} 
```