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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.
This D2P dataset example contains a small version of the original D2P dataset intended for easier overview. Here, only a subset of the scenes are included. The structure within the scenes is the same. To use the D2P Dataset, please, visit the page of the original dataset.
paper (coming later) | github | webpage
Dataset Structure
d2p_dataset_example
βββ statues/
β βββ scene1/
β β βββ images/
β β β βββ img1.png
β β β βββ img2.png
β β β βββ ...
β β βββ sparse/
β β β βββ cameras.txt
β β β βββ frames.txt
β β β βββ images.txt
β β β βββ points3D.txt
β β β βββ rigs.txt
β β βββ scene1_image_list.txt
β β βββ scene1_image_pairs.txt
β βββ scene2/
β β βββ ...
β βββ ...
βββ vegetation/
Dataset Fields
Each scene contains:
images/: RGB imagessparse/: 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/ folderscene1_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 π€ Datasets
from datasets import load_dataset
ds = load_dataset("floodgab/d2p_dataset_example")
print(ds["validation"][0])
Loading Example
To download the Depth2Pose dataset
from huggingface_hub import snapshot_download
path = snapshot_download("floodgab/d2p_dataset_example")
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},
}
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