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Prepare Scene Flow and KITTI dataset.

It is recommended to symlink the dataset root to $DenseMatchingBenchmark/datasets/. Related preparing tools for json file generation can be found in tools

├── KITTI-2012
│   └── data_stereo_flow
│       ├── testing
│       └── training
├── KITTI-2015
│   ├── calib
│   ├── devkit
│   ├── testing
│   │   ├── image_2
│   │   └── image_3
│   └── training
│       ├── disp_noc_0
│       ├── disp_noc_1
│       ├── image_2
│       └── image_3
└── SceneFlow
    ├── calib
    ├── driving
    │   ├── disparity
    │   ├── frames_cleanpass
    │   └── frames_finalpass
    ├── flyingthings3d
    │   ├── disparity
    │   ├── frames_cleanpass
    │   └── frames_finalpass
    └── Monkaa
        ├── disparity
        ├── frames_cleanpass
        └── frames_finalpass

Prepare visualization dataset.

We enable evaluation and visualization for each epoch. Especially, the visualization means visualize the estimated results.

It is recommended to download the visualization data we prepared, btw, you can also prepare by yourself.

How To Use

To use, you just have to make the param 'data=dict(vis=...)' in config file valid.

Down Link

The down-link for visualization data including:

  1. Baidu YunPan: https://pan.baidu.com/s/1J7OBum7-kTFQV3Sbr3qT4w password: 0q8y
  2. Google Drive: https://drive.google.com/open?id=1oroPkS9bYBULvRW2olpA2wLgKSxU9Ovl
visualization_data
├── KITTI-2015
│   ├── annotations
│   ├── calib
│   ├── disparity
│   ├── genVisKITTI2015AnnoFile.py
│   ├── genVisKITTIVOAnnoFile.py
│   ├── images
│   └── velodyne_points
└── SceneFlow
    ├── __init__.py
    ├── annotations
    ├── disparity
    ├── genVisSFAnnoFile.py
    ├── images
    ├── occ
    └── readme.txt