CREStereo Repository for the 'Towards accurate and robust depth estimation' project
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README.md

CREStereo-Pytorch

Non-official Pytorch implementation of the CREStereo (CVPR 2022 Oral) model converted from the original MegEngine implementation.

!CREStereo-Pytorch stereo detph estimation Stereo depth estimation on the cones images from the Middlebury dataset (https://vision.middlebury.edu/stereo/data/scenes2003/)

Important

  • This is just an effort to try to implement the CREStereo model into Pytorch from MegEngine due to the issues of the framework to convert to other formats (https://github.com/megvii-research/CREStereo/issues/3).
  • I am not the author of the paper, and I am don't fully understand what the model is doing. Therefore, there might be small differences with the original model that might impact the performance.
  • I have not added any license, since the repository uses code from different repositories. Check the License section below for more detail.

Pretrained model

  • Download the model from here and save it into the models folder.
  • The model was covnerted from the original MegEngine weights using the convert_weights.py script. Place the MegEngine weights (crestereo_eth3d.mge) file into the models folder before the conversion.

Licences:

References: