## Data Format

Here we explain the `poses_bounds.npy` file format. This file stores a numpy array of size Nx17 (where N is the number of input videos). You can load the data using the following codes.

```
poses_arr = np.load(os.path.join(basedir, 'poses_bounds.npy'))
poses = poses_arr[:, :-2].reshape([-1, 3, 5]).transpose([1,2,0])
bds = poses_arr[:, -2:].transpose([1,0])
```

Each row of length 17 gets reshaped into a 3x5 pose matrix and 2 depth values that bound the closest and farthest scene content from that point of view.

The pose matrix is a 3x4 camera-to-world affine transform concatenated with a 3x1 column `[image height, image width, focal length]` to represent the intrinsics (we assume the principal point is centered and that the focal length is the same for both x and y). 

<big>NOTE: In our dataset, the focal length for different cameras are different!!!</big>

The right-handed coordinate system of the the rotation (first 3x3 block in the camera-to-world transform) is as follows: from the point of view of the camera, the three axes are `[down, right, backwards]` which some people might consider to be `[-y,x,z]`, where the camera is looking along `-z`. (The more conventional frame `[x,y,z]` is `[right, up, backwards]`. The COLMAP frame is `[right, down, forwards]` or `[x,-y,-z]`.)

We also provide an example of our dataloader in `dataloader.py`.