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Prepare datasets

It is recommended to symlink the dataset root to $MONOCULAR-DEPTH-ESTIMATION-TOOLBOX/data. If your folder structure is different, you may need to change the corresponding paths in config files.

monocular-depth-estimation-toolbox
├── depth
├── tools
├── configs
├── splits
├── data
│   ├── kitti
│   │   ├── input
│   │   │   ├── 2011_09_26
│   │   │   ├── 2011_09_28
│   │   │   ├── ...
│   │   │   ├── 2011_10_03
│   │   ├── gt_depth
│   │   │   ├── 2011_09_26_drive_0001_sync
│   │   │   ├── 2011_09_26_drive_0002_sync
│   │   │   ├── ...
│   │   │   ├── 2011_10_03_drive_0047_sync
|   |   ├── benchmark_test
│   │   │   ├── 0000000000.png
│   │   │   ├── 0000000001.png
│   │   │   ├── ...
│   │   │   ├── 0000000499.png
|   |   ├── benchmark_cam
│   │   │   ├── 0000000000.txt
│   │   │   ├── 0000000001.txt
│   │   │   ├── ...
│   │   │   ├── 0000000499.txt
│   │   ├── split_file.txt
│   ├── nyu
│   │   ├── basement_0001a
│   │   ├── basement_0001b
│   │   ├── ... (all scene names)
│   │   ├── split_file.txt
│   ├── SUNRGBD
│   │   ├── SUNRGBD
│   │   │   ├── kv1
│   │   │   ├── kv2
│   │   │   ├── realsense
│   │   │   ├── xtion
│   │   ├── split_file.txt
│   ├── cityscapes
│   │   ├── camera
│   │   │   ├── test
│   │   │   ├── train
│   │   │   ├── val
│   │   ├── disparity_trainvaltest
│   │   │   ├── disparity
│   │   ├── leftImg8bit_trainvaltest
│   │   │   ├── leftImg8bit
│   │   ├── split_file.txt
│   ├── cityscapesExtra
│   │   ├── camera
│   │   │   ├── train_extra
│   │   ├── disparity
│   │   │   ├── train_extra
│   │   ├── leftImg8bit
│   │   │   ├── train_extra
│   │   ├── split_file.txt
│   ├── custom_dataset
│   │   ├── train
│   │   │   ├── rgb
│   │   │   │   ├── 0.xxx
│   │   │   │   ├── 1.xxx
│   │   │   │   ├── 2.xxx
│   │   │   ├── depth
│   │   │   │   ├── 0.xxx
│   │   │   │   ├── 1.xxx
│   │   │   │   ├── 2.xxx
│   │   ├── val
│   │   │   ├── rgb
│   │   │   │   ├── 0.xxx
│   │   │   │   ├── 1.xxx
│   │   │   │   ├── 2.xxx
│   │   │   ├── depth
│   │   │   │   ├── 0.xxx
│   │   │   │   ├── 1.xxx
│   │   │   │   ├── 2.xxx

KITTI

Download the offical dataset from this link, including the raw data (about 200G) and fine-grained ground-truth depth maps.

Then, unzip the files into data/kitti. Remember to organizing the directory structure following instructions (Only need a few cut operations).

Finally, copy split files (whose names are started with kitti) in splits folder into data/kitti. Here, I utilize eigen splits following other supervised methods.

Some methods may use the camera intrinsic parameters (i.e., BTS), you need to download the benchmark_cam consisting of camera intrinsic parameters of the benchmark test set.

NYU

Following previous work, I utilize about 24231 image-depth pairs as the training set and standard 652 images as the validation set. You can download the dataset from Google Drive Link (including training and validation sets).

SUNRGBD

The dataset could be download from this link. Copy SUNRGBD_val_splits.txt in splits into data/SUNRGBD.

Cityscapes and CityscapesExtra

The data could be found here after registration. Copy cityscapes_train.txt and cityscapes_train in splits to data/cityscapes. If using extra data, copy cityscapes_train_extra.txt to data/cityscapesExtra.

Custom Dataset

We also provide a simple custom dataset class for users in depth/datasets/custom.py. Organize your data folder as our illustration. Note that instead of utilizing a split file to divide the train/val set, we directly classify data into train/val folder. A simple config file can be like:

train=dict(
    type=dataset_type,
    pipeline=dict(...),
    data_root='data/custom_dataset',
    test_mode=False,
    min_depth=1e-3,
    max_depth=10,
    depth_scale=1)

As for the custom dataset, we do not implement the evaluation details. If you want to get a quantitive metric result, you need to implement the pre_eval and evaluate functions following the ones in KITTI or other datasets.