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25 lines
2.0 KiB
25 lines
2.0 KiB
# CREStereo-Pytorch
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Non-official Pytorch implementation of the CREStereo (CVPR 2022 Oral) model converted from the original MegEngine implementation.
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*Stereo depth estimation on the cones images from the Middlebury dataset (https://vision.middlebury.edu/stereo/data/scenes2003/)*
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# Important
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- 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).
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- 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.
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- I have not added any license, since the repository uses code from different repositories. Check the License section below for more detail.
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# Pretrained model
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- Download the model from [here](https://drive.google.com/file/d/1D2s1v4VhJlNz98FQpFxf_kBAKQVN_7xo/view?usp=sharing) and save it into the **[models](https://github.com/ibaiGorordo/CREStereo-Pytorch/tree/main/models)** folder.
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- The model was covnerted from the original **[MegEngine weights](https://drive.google.com/file/d/1Wx_-zDQh7BUFBmN9im_26DFpnf3AkXj4/view)** using the `convert_weights.py` script. Place the MegEngine weights (crestereo_eth3d.mge) file into the **[models](https://github.com/ibaiGorordo/CREStereo-Pytorch/tree/main/models)** folder before the conversion.
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# Licences:
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- CREStereo (Apache License 2.0): https://github.com/megvii-research/CREStereo/blob/master/LICENSE
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- RAFT (BSD 3-Clause):https://github.com/princeton-vl/RAFT/blob/master/LICENSE
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- LoFTR (Apache License 2.0):https://github.com/zju3dv/LoFTR/blob/master/LICENSE
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# References:
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- CREStereo: https://github.com/megvii-research/CREStereo
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- RAFT: https://github.com/princeton-vl/RAFT
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- LoFTR: https://github.com/zju3dv/LoFTR
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- torch2mge: https://github.com/MegEngine/torch2mge
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