Datasets:

floodgab
/

d2p_dataset

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 | webpage

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

Load with 🤗 Datasets

from datasets import load_dataset

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

Loading Example

To download the Depth2Pose dataset

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

@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},
}