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license: cc-by-4.0
language:
- en
tags:
- camera-pose-estimation
- viewpoint_recommendation
pretty_name: ViewRecDB-100K
size_categories:
- 10K<n<100K
configs:
- config_name: default
data_files:
- split: train
path: train/metadata.csv
- split: validation
path: val/metadata.csv
- split: test
path: test/metadata.csv
---
# ViewRecDB-100K
<div align="center">
<img src="./docs/fig1.png" alt="fig1" width="95%">
</div>
## Dataset Summary
`ViewRecDB-100K` is a 3D viewpoint recommendation dataset for AI photography. It contains 100K training samples and 1K test samples. Each sample consists of a paired suboptimal and optimal image, along with the corresponding 3D viewpoint change annotation. Given a suboptimal image, the task is to predict the 3D viewpoint change toward the optimal image.
The dataset is automatically constructed from the `Unsplash Full Dataset`. The test samples form a dedicated benchmark, where each optimal image has been verified by experts to have better photographic composition than the corresponding suboptimal image.
## Dataset Structure
## Data Structure
The dataset is organized into `train`, `val`, and `test` splits. Each sample folder contains one optimal image and `1 or 2` generated suboptimal images with corresponding viewpoint change annotations.
```text
ViewRecDB-100K/
├── train/
│ └── photo_id/ # photo_id in Unsplash Full Dataset
│ ├── original.jpeg # optimal image
│ ├── generated_0.jpeg # suboptimal image
│ ├── generated_0.json # viewpoint change annotation
│ ├── generated_1.jpeg # optional suboptimal image
│ └── generated_1.json # optional viewpoint change annotation
├── val
└── test
```
### Data Instances
A sample from the training set is provided below:
<div align="center">
<table>
<tr>
<td align="center"><strong>Suboptimal Image</strong></td>
<td align="center"><strong>Optimal Image</strong></td>
<td align="center"><strong>Viewpoint Change</strong></td>
</tr>
<tr>
<td><img src="./docs/example1.jpeg" width="360"></td>
<td><img src="./docs/example2.jpeg" width="260"></td>
<td align="left">
<pre><code>{
"change_orientation": true,
"pose": [
0.06223759800195694,
-0.004903144668787718,
-0.06147797778248787,
-0.007686913013458252,
-0.1884094774723053,
-0.017775092273950577,
0.981899619102478,
0.4500928404485889,
0.651864323785646
]
}</code></pre>
</td>
</tr>
</table>
</div>
### Data Fields
Each data instance contains the following fields:
- `suboptimal_image` / `optimal_image`: The input suboptimal image. It is an RGB image with a total pixel budget of `1024 × 1024`.
- `viewpoint_change`: The 3D viewpoint change annotation from the suboptimal image to the optimal image.
- `change_orientation`: A boolean value indicating whether the image orientation should be changed, i.e., switching between landscape and portrait.
- `pose`: A 9-dimensional viewpoint descriptor:
- `pose[0:3]`: translation vector.
- `pose[3:7]`: rotation quaternion.
- `pose[7:9]`: FoV scaling factor `s`, which denotes the FoV shrinking ratio, equivalent to the focal length scaling ratio.
## Uses
Due to the redistribution restrictions of the `Unsplash Full Dataset`, the `original.jpeg` files in `ViewRecDB-100K` are not directly included in this release. Users need to apply for and download the `Unsplash Full Dataset` from the [official source](https://unsplash.typeform.com/to/HPVbjo?typeform-source=unsplash.com).
- After obtaining the Unsplash metadata files, specify their path with `--csv_root` and set `--id_root` to the split directory where the original images should be downloaded.
```bash
python -m script.download \
--csv_root /path/to/unsplash_metadata \
--id_root /path/to/ViewRecDB-100K/train | val | test \
--csv_pattern "*.csv*"
```
Arguments:
- `--csv_root`: Path to the directory containing the Unsplash metadata files.
- `--id_root`: Path to the dataset split directory, such as `train`, `val`, or `test`.
- `--csv_pattern`: Filename pattern used to match the metadata files, such as `*.csv*` or `*.tsv*`. |