NOTE! This project can be used as data augmentation for generating real depth camera like depth images

Generating SGM calculated disparity estimation from monocular RGB image. This project provides a python based library which generates stereo disparity map from a single image input.

This project is highly based on "Watson, Jamie, et al. "Learning stereo from single images." European Conference on Computer Vision. Springer, Cham, 2020". Refer to it for more mathmatical details.

• This project is a data augmentation projection for depth-related image observations.
• To simulate a real traditional stereo-camera based depth estimation result from the estimated dense depth map.

The stereo generator assumes the following data are given:

• 1 RGB image as the left eye view (can be right eye as well, does not matter)
• 1 corresponding predicted disparity map (any monocular depth estimation is appliable).
• The depth estimation module to be added here...

• Refer to example.py for more detailed examples. Please check the comments in example.py

python example.py

• Feel free to change the image path using the images in folder "testsets" a link

Using OpenCV provided SGBM (no post-processing), we can synthesize a rough disparity:

Using a 3rd party SGM (with post-processing) and well finetuned post-processing filtering (e.g.MSE), we can synthesize relatively well disparity maps, but very time-consuming:

If bad quality disparity map generation map is required, test these parameters:

1. increase the max_disparity_range lower bound value, when it is > 80, e.g.max_disparity_range=(80, 190), the quality gets bad (because the are more holes in generated right eye view)
2. make fix_disparity_scale True (so that will always generate high disparity views)
3. modify the hyper-paramters of SGM in function st_converter.convert_stereo(), different prefiltering and post-filtering will affect the quality of disparity estimation
4. augmenting the synthesized right eye view, to add noise to the right eye imgae, then disparity estimation may perform bad: disable_synthetic_augmentation=False

• Add monocular depth/disparity estimation modules

• The right eye view generation was inspired by work https://github.com/nianticlabs/stereo-from-mono
• The right eye view image generation code is based on https://github.com/nianticlabs/stereo-from-mono
• The SGBM method is implemented using OpenCV library https://docs.opencv.org/4.x/d9/d0c/group__calib3d.html
• The 3rd party SGBM method is based on https://github.com/beaupreda/semi-global-matching
• The image white balance model is based on https://github.com/mahmoudnafifi/WB_sRGB

If you use this projection, please cite the references mentioned above, and this repository:

@misc{github,
  author={github},
  title={mono2SGM},
  year={2022},
  url={https://github.com/uenian33/mono2SGM},
}