# Preparing NeRF On-The-Road (NOTR) Dataset

This doc is heavily borrowed from [emernerf](https://github.com/NVlabs/EmerNeRF/blob/main/docs/NOTR.md?plain=1), thanks for the author!

## 1. Register on Waymo Open Dataset

### Sign Up for a Waymo Open Dataset Account and Install gcloud SDK

To download the Waymo dataset, you need to register an account at [Waymo Open Dataset](https://waymo.com/open/). You also need to install gcloud SDK and authenticate your account. Please refer to [this page](https://cloud.google.com/sdk/docs/install) for more details.

### Set Up the Data Directory

Once you've registered and installed the gcloud SDK, create a directory to house the raw data:

```shell
# Create the data directory or create a symbolic link to the data directory
mkdir -p ./data/waymo/raw   
mkdir -p ./data/waymo/processed 
```

## 2. Download the raw data

Start by downloading the necessary data samples as follows:

### Downloading Specific Scenes from Waymo Open Dataset

For example, to obtain the 114th, 700th, and 754th scenes from the Waymo Open Dataset, execute:

```shell
python data/download_waymo.py \
    --target_dir ./data/waymo/raw \
    --scene_ids 114 700 754
```

### Downloading Different Splits of the NOTR Dataset

Our NOTR dataset comes in multiple splits. Specify the `split_file` argument to download your desired split:

- **Static32 Split:**

```shell
python datasets/download_waymo.py --split_file data/waymo_splits/static32.txt
```

- **Dynamic32 Split:**

```shell
python datasets/download_waymo.py --split_file data/waymo_splits/dynamic32.txt
```

Ensure you modify the paths and filenames to align with your project directory structure and needs.

### Manual download
If you cannot download by this way, you can download raw data from [here](https://console.cloud.google.com/storage/browser/waymo_open_dataset_scene_flow/train?pageState=(%22StorageObjectListTable%22:(%22f%22:%22%255B%255D%22))&prefix=&forceOnObjectsSortingFiltering=true) manually.

### Dataset split

For the Waymo Open Dataset, we first organize the scene names alphabetically and store them in `data/waymo_train_list.txt`. The scene index is then determined by the line number minus one. The splits for the NOTR dataset are as follows:

**Static-32**: 3, 19, 36, 69, 81, 126, 139, 140, 146, 148, 157, 181, 200, 204, 226, 232, 237, 241, 245, 246, 271, 297, 302, 312, 314, 362, 482, 495, 524, 527, 753, 780

**Dynamic-32**: 16, 21, 22, 25, 31, 34, 35, 49, 53, 80, 84, 86, 89, 94, 96, 102, 111, 222, 323, 323, 382, 382, 402, 402, 427, 427, 438, 438, 546, 581, 592, 620, 640, 700, 754, 795, 796

**Diverse-56**:

- Ego-static: 1, 23, 24, 37, 66, 108, 114, 115
- Dusk/Dawn: 124, 147, 206, 213, 574, 680, 696, 737
- Gloomy: 47, 205, 220, 284, 333, 537, 699, 749
- Exposure mismatch: 58, 93, 143, 505, 545, 585, 765, 766
- Nighttime: 7, 15, 30, 51, 130, 133, 159, 770
- Rainy: 44, 56, 244, 449, 688, 690, 736, 738
- High-speed: 2, 41, 46, 62, 71, 73, 82, 83

For further information, refer to the `data/waymo_splits` directory.

## 3. Preprocess the data

After downloading the raw dataset, you'll need to preprocess this compressed data to extract and organize various components.

### Running the Preprocessing Script

To preprocess specific scenes of the dataset, use the following command:

```shell
python preprocess.py \
    --data_root data/waymo/raw/ \
    --target_dir data/waymo/processed \
    --split training \
    --process_keys images lidar calib pose dynamic_masks \
    --workers 2 \
    --scene_ids 114 700
```

Alternatively, preprocess different splits of the NOTR dataset by providing the split file:

```shell
# preprocess the static split
python preprocess.py \
    --data_root data/waymo/raw/ \
    --target_dir data/waymo/processed \
    --split training \
    --process_keys images lidar calib pose dynamic_masks \
    --workers 16 \
    --split_file data/waymo_splits/static32.txt # change to dynamic32.txt or diverse56.txt to preprocess different splits
```

**Troubleshooting**: if you encounter `TypeError: 'numpy._DTypeMeta' object is not subscriptable`, use `pip install numpy==1.26.1` and ignore the warnings.

This command performs the following tasks:

- Extract camera poses, images, LiDAR data, calibration matrices, dynamic masks and point cloud flows from the raw dataset.
- Stores the extracted data in the `data/waymo/processed` directory.

### Data Components

After preprocessing, the dataset will be organized into the following components:

- **Images**: All frame images named as  `{timestep:03d}_{cam_id}.jpg`, where cam_id is 0, 1, 2, 3, 4 for FRONT, FRONT_LEFT, FRONT_RIGHT, SIDE_LEFT, SIDE_RIGHT cameras respectively.
- **Ego Poses**: - **Ego Poses**: Each file is named `{timestep:03d}.txt` and contains a 4x4 ego to world transformation matrix.
- **Camera Intrinsics**: Each file is named `{cam_id}.txt` and contains a 1d array of [f_u, f_v, c_u, c_v, k{1, 2}, p{1, 2}, k{3}].
- **Camera Extrinsics**: Each file is named `{cam_id}.txt` and contains a 4x4 camera to ego transformation matrix, i.e., `frame.context.camera_calibrations.extrinsic.transform` from the Waymo Open Dataset.
- **Lidar Data**: Each file is named {timestep:03d}.bin and contains an Nx14 array with:
  - **Origins** (3 dims): Origins of LiDAR rays in the ego-vehicle coordinate system.
  - **Points** (3 dims): (x, y, z) coordinates of LiDAR points in the ego-vehicle coordinate system.
  - **Flows** (4 dims): Flow vectors (dx, dy, dz, flow_class). Refer to lines 676-682 of `datasets/waymo_preprocess.py` for flow_class definition. Used for evaluating the flow prediction performance.
  - **Ground Labels** (1 dim): the ground labels of all LiDAR points. 1 means ground and 0 means non-ground. This is used for training neural scene flow priors, which is not used in EmerNeRF.
  - **Intensities** (1 dim): Intensity values of LiDAR points.
  - **Elongations** (1 dim): Elongations of LiDAR points.
  - **Laser_ids** (1 dim): Laser IDs of LiDAR points with 0: TOP, 1: FRONT, 2: SIDE_LEFT, 3: SIDE_RIGHT, 4: REAR.
- **Dynamic Mask**: Binary mask images named `{timestep:03d}_{cam_id}.png` to indicate the dynamic regions in the scene. 1 means dynamic and 0 means static. These are obtained by filtering ground truth 2D object bounding boxes by excluding the bounding boxes with velocity less than 1m/s, so as
to include meaningful moving objects without introducing too much background noise. This is used for evaluation, which will not be used during training.
- **Sky Mask**: Binary mask images named `{timestep:03d}_{cam_id}.png` to indicate the sky regions in the scene. 1 means sky and 0 means non-sky.

### Directory Structure

The organized dataset will follow this directory structure:

```
data/waymo/processed
├── training
│   ├── SCENE_ID
│   │   ├── dynamic_masks      # Dynamic masks: `{timestep:03d}_{cam_id}.png`
│   │   ├── ego_pose           # Ego vehicle poses: `{timestep:03d}.txt`
│   │   ├── extrinsics         # Camera extrinsics: `{cam_id}.txt`
│   │   ├── images             # Images: `{timestep:03d}_{cam_id}.jpg`
│   │   ├── intrinsics         # Camera intrinsics: `{cam_id}.txt`
│   │   ├── lidar              # LiDAR data: `{timestep:03d}.bin`
│   │   ├── sky_masks          # Sky masks: we don't use here
│   │   ├── FEATURE_NAME       # Features: `{timestep:03d}_{cam_id}.npy` 
│   │   └── occ3d              # 3D semantic occupancy grids: `{timestep:03d}.npz` or `{timestep:03d}_04.npz`
```

Note that the `FEATURE_NAME` folder will be generated when call the training script.