CREStereo-Pytorch

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

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:

• CREStereo (Apache License 2.0): https://github.com/megvii-research/CREStereo/blob/master/LICENSE
• RAFT (BSD 3-Clause):https://github.com/princeton-vl/RAFT/blob/master/LICENSE
• LoFTR (Apache License 2.0):https://github.com/zju3dv/LoFTR/blob/master/LICENSE

References:

• CREStereo: https://github.com/megvii-research/CREStereo
• RAFT: https://github.com/princeton-vl/RAFT
• LoFTR: https://github.com/zju3dv/LoFTR
• torch2mge: https://github.com/MegEngine/torch2mge