- The captured scenes include: carving, boots, baskets, cinema, chef. The simulated scenes include lego, chair, ficus, hotdog, bench. These have different structures and loaders.
In the root of the dataset folder you can also find the intrinsics.npy which is the set of captured rays and parameters used in training and pulse_low_flux.mat which is the calibrated laser pulse.
- Training transform files.
For the captured scenes the training transforms are all named transforms_train.json and can be found under <scene_name>/final_cams/<num_views>/transforms_train.json.
For the simulated scenes the training transforms are all named transforms_train_v{i}.json where i is the number of views (2, 3, 5). and can be found under <scene_name>/transforms_train_v{i}.json.
- Test transform files.
For the captured scenes the test transforms can be found under <scene_name>/final_cams/test_jsons/transforms_test.json.
For the simulated scenes the test transforms can be found under <scene_name>/transforms_test_final.json.
- Downloading.
- You can download the data yourself either through the Dropbox download button, or by right-clicking a folder and selecting copy link address, then
wget "copied link"
will start a download of the folder.
- (!!!) Using the dataset.
To use the dataset alongside its transforms please look at the loader in loaders/loader_captured.py in the GitHub repository. Most importantly to use the temporal captured data, you will have to resample the transient using the shift given in the intrinsics.npy file:
img_shape = (512, 512)
exposure_time= 299792458*4e-12
n_bins = 1500
x = (torch.arange(img_shape[0], device="cpu")-img_shape[0]//2+0.5)/(img_shape[0]//2-0.5)
y = (torch.arange(img_shape[0], device="cpu")-img_shape[0]//2+0.5)/(img_shape[0]//2-0.5)
z = torch.arange(n_bins*2, device="cpu").float()
X, Y, Z = torch.meshgrid(x, y, z, indexing="xy")
Z = Z*exposure_time/2
Z = Z - shift[0]
Z = Z*2/exposure_time
Z = (Z-n_bins*2//2+0.5)/(n_bins*2//2-0.5)
grid = torch.stack((Z, X, Y), dim=-1)[None, ...]
del X
del Y
del Z
rgb = torch.Tensor(rgb)[..., :3000].float().cpu()
rgb = torch.nn.functional.grid_sample(rgb[None, None, ...], grid, align_corners=True).squeeze().cpu()
where shift is the value from the intrinsics.npy file and rgb is the original loaded transient.