Add task category and paper link to dataset card
#1
by nielsr HF Staff - opened
README.md
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@@ -1,5 +1,7 @@
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---
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license: cc-by-4.0
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dataset_info:
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features:
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- name: image
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dtype: int64
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- name: fx
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dtype: float64
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-
- name: fy
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dtype: float64
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- name: cx
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dtype: float64
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@@ -28,7 +30,7 @@ dataset_info:
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dtype: float64
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- name: qx
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dtype: float64
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-
- name: qy
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dtype: float64
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- name: qz
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dtype: float64
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- split: evaluation
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path: data/evaluation-*
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---
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# The D2P dataset
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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.
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This **D2P dataset example** contains a small version of [the original **D2P dataset**](https://huggingface.co/datasets/floodgab/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.
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[**
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## Dataset Structure
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from datasets import load_dataset
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ds = load_dataset("floodgab/d2p_dataset_example")
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print(ds["
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```
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### Loading Example
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---
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license: cc-by-4.0
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task_categories:
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- depth-estimation
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dataset_info:
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features:
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- name: image
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dtype: int64
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- name: fx
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dtype: float64
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- name: fy}
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dtype: float64
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- name: cx
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dtype: float64
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dtype: float64
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- name: qx
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dtype: float64
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- name: qy}
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dtype: float64
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- name: qz
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dtype: float64
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- split: evaluation
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path: data/evaluation-*
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---
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+
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# The D2P dataset
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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.
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This **D2P dataset example** contains a small version of [the original **D2P dataset**](https://huggingface.co/datasets/floodgab/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.
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[**Paper**](https://huggingface.co/papers/2605.19797) | [**GitHub**](https://github.com/kocurvik/depth2pose) | [**Project Page**](https://kocurvik.github.io/depth2pose/)
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## Dataset Structure
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from datasets import load_dataset
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ds = load_dataset("floodgab/d2p_dataset_example")
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print(ds["evaluation"][0])
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```
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### Loading Example
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