DIFFNet

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

A new backbone for self-supervised depth estimation.

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

• Data preparation: please refer to monodepth2

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:

• monodepth2
• HRNet