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:

brandleyzhou / DIFFNet

readme

DIFFNet

Update:

Comparing with others

Evaluation on selected hard cases:

Trained weights on KITTI

Setting up before training and testing

Training:

Testing:

Infer a single depth map from a RGB:

Acknowledgement