README.md

---
license: cc-by-4.0
---

# Charge: A Comprehensive Benchmark and Dataset for Dynamic Novel View Synthesis 

This repository contains scripts for downloading [Charge](https://charge-benchmark.github.io/) dataset.

## Updates

- [x] [16/05/2025]Full data release
- [x] [12/12/2024] Release of the first scene in the dataset.

## Download

The only dependency of download scripts is the Huggingface Hub client which can be installed as:
```
pip install huggingface-hub
```

You can manually download desired scene, task, and modalities via:
```
python downloader.py --output-dir $1 --scenes [list-of-scenes] --modalities [rgb depth mask segmentation normal flow_fw flow_bw] --tasks [Dense Sparse Mono]
```
E.g. to download RGB images and dynamic masks for scene 050_0130 in the task of Sparse reconstruction (in the local folder), use:
 ```
python downloader.py --output-dir $1 --scenes 050_0130 --modalities rgb mask --tasks Sparse
```

For convenience, we provide bash scripts to download all data, or RGB data per task:
- `download_all.sh`
- `download_rgb_dense.sh`
- `download_rgb_sparse.sh`
- `download_rgb_mono.sh`

Use them as following:
```
bash download_all.sh path-to-download-location
```

## Data structure

The data is structured as following:
```
Charge_v1_0
|-- 050_0130
    |-- Dense
        |-- Dense_00_00
            |-- frame_0416.png
            |-- frame_0416_depth.npy
            |-- frame_0416_segmentation.png
            |-- frame_0416_normal.png
            |-- frame_0416_dyn_mask.png
            |-- frame_0416_flow_fw.npy
            |-- frame_0416_flow_bw.npy
            |   ...
        |   ...
        |-- transforms_train.json
        |-- transforms_test.json
    |-- Sparse
        |  ...
    |-- Mono
        |  ...
    |-- segmentation.json
| ..
```

The modalities included are as following:
- `frame_XXXX.png` - RGB image (2048x858)
- `frame_XXXX_depth.npy` - metric depth
- `frame_XXXX_segmentation.png` - segmentation map encoded as uint16 image (objects dictionary included in `segmentation.json`)
- `frame_XXXX_normal.png` - normal map encoded as uint16 image
- `frame_XXXX_dyn_mask.png` - mask of dynamic content
- `frame_XXXX_flow_fw.npy` - optical flow (forward)
- `frame_XXXX_flow_bw.npy` - optical flow (backward)

We include the camera data in the `.json` files found in task directories. In Sparse setup we include 3 scenarios (corresponding to 3, 6, 9 input views). In Mono scenario we include 4 different camera trajectories. For each, for convenience we include 3 testing scenarios: `_lite` - only rig cameras (stereo cameras with different baseline, and orbital camera), `_med` - rig cameras + 4 central cameras from Dense setup (static cameras), `_full` - rig cameras + all Dense cameras. We also include splits for Stereo training and evaluation. 

Each transforms `.json` contains a dictionary:
```
{
    "CameraName": [ #List of camera parameters per frame
        {   
            "fov" - field of view
            "f" - focal length
            "K" - intrinsics
            "transformation_matrix" - extrinsics
            "image_path" - corresponding image
        },
        {
            # Camera data for second frame
        },
        ...
    ], 
    "CameraName2": ...,
    ...
}
```

## Download size

The dataset is split into several repositories due to size. Below table summarises the size of data in GB. *-optical flow in one direction only (data include both forward and backward flow)

<table>
<tr>
<th colspan="2" style="text-align: center; width:12%"> Scene </th> 
<th style="text-align: right; width:11%"> RGB </th>
<th style="text-align: right; width:11%"> Depth </th>
<th style="text-align: right; width:11%"> Segm </th>
<th style="text-align: right; width:11%"> Normal </th>
<th style="text-align: right; width:11%"> Mask </th>
<th style="text-align: right; width:11%"> Flow* </th>
<th style="text-align: right; width:11%"> Total </th>
</tr>
<tr>
<td rowspan="4" style="writing-mode: vertical-rl; transform: rotate(180deg); text-align: center;"> 050_0130 </td> <td> Dense </td>
<td style="text-align: right"> 4.1 </td>
<td style="text-align: right"> 25.2 </td>
<td style="text-align: right"> 0.6 </td>
<td style="text-align: right"> 14.5 </td>
<td style="text-align: right"> 0.1 </td>
<td style="text-align: right"> 50.3 </td>
<td style="text-align: right"> 144.9 </td>
</tr>
<tr>
<td> Sparse </td>
<td style="text-align: right"> 2.0 </td>
<td style="text-align: right"> 11.7 </td>
<td style="text-align: right"> 0.3 </td>
<td style="text-align: right"> 7.0 </td>
<td style="text-align: right"> 0.1 </td>
<td style="text-align: right"> 23.3 </td>
<td style="text-align: right"> 67.5 </td>
</tr>
<tr>
<td> Mono </td>
<td style="text-align: right"> 2.0 </td>
<td style="text-align: right"> 12.3 </td>
<td style="text-align: right"> 0.3 </td>
<td style="text-align: right"> 7.0 </td>
<td style="text-align: right"> 0.1 </td>
<td style="text-align: right"> 24.5 </td>
<td style="text-align: right"> 70.6 </td>
</tr>
<tr>
<td> Total </td>
<td style="text-align: right"> 8.1 </td>
<td style="text-align: right"> 49.1 </td>
<td style="text-align: right"> 1.2 </td>
<td style="text-align: right"> 28.4 </td>
<td style="text-align: right"> 0.2 </td>
<td style="text-align: right"> 98.1 </td>
<td style="text-align: right"> 283.0 </td>
</tr>

</table>