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[BMVC 2021] ''Self-Supervised Monocular Depth Estimation with Internal Feature Fusion''

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DIFFNet

This repo is for Self-Supervised Monocular Depth Estimation with Internal Feature Fusion(arXiv), BMVC2021

A new backbone for self-supervised depth estimation.

PWC

If you think it is a useful work, please consider citing it.

@inproceedings{zhou_diffnet,
    title={Self-Supervised Monocular Depth Estimation with Internal Feature Fusion},
    author={Zhou, Hang and Greenwood, David and Taylor, Sarah},
    booktitle={British Machine Vision Conference (BMVC)},
    year={2021}
    }

Update:

  • [16-05-2022] Adding cityscapes trainining and testing based on Manydepth.

  • [22-01-2022] A model diffnet_649x192 uploaded (slightly improved than that of orginal paper)

  • [07-12-2021] A multi-gpu training version availible on multi-gpu branch.

Comparing with others

Evaluation on selected hard cases:

Trained weights on KITTI

  • Please Note: the results of diffnet_1024x320_ms are not reported in paper *
Methods abs rel sq rel RMSE rmse log D1 D2 D3
1024x320 0.097 0.722 4.345 0.174 0.907 0.967 0.984
1024_320_ms 0.094 0.678 4.250 0.172 0.911 0.968 0.984
1024x320_ms_ttr 0.079 0.640 3.934 0.159 0.932 0.971 0.984
640x192 0.102 0.753 4.459 0.179 0.897 0.965 0.983
640x192_ms 0.101 0.749 4.445 0.179 0.898 0.965 0.983

Setting up before training and testing

Training:

sh start2train.sh

Testing:

sh disp_evaluation.sh

Infer a single depth map from a RGB:

sh test_sample.sh

Acknowledgement

Thanks the authors for their works:

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