Create README.md

README.md

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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
+
+---
+
+# 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*`.