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StereoNet: Guided Hierarchical Refinement for Real-Time Edge-Aware Depth prediction model in pytorch. ECCV2018

If you want to communicate with me about the StereoNet, please concact me without hesitating. My email:

xuanyili.edu@gmail.com

my model result

Now, my model's speed can achieve 60-25FPS on 540*960 img with the best result of 1.87 EPE_all with 16X multi model， 1.95 EPE_all with 16X single model 1.32 EPE_all with 8X single model 1.48EPE_all with 8X multi model on sceneflow dataset by end-to-end training. the following are the side outputs and the prediction example

train example

train example

test example(outputs of 16single model and GT)

test example

Citation

If you find our work useful in your research, please consider citing:

@inproceedings{khamis2018stereonet, title={Stereonet: Guided hierarchical refinement for real-time edge-aware depth prediction}, author={Khamis, Sameh and Fanello, Sean and Rhemann, Christoph and Kowdle, Adarsh and Valentin, Julien and Izadi, Shahram}, booktitle={Proceedings of the European Conference on Computer Vision (ECCV), Munich, Germany}, pages={8--14}, year={2018} }

Introduction

I implement the real-time stereo model according to the StereoNet model in pytorch. The speed can reach 30FPS with top performance. The speed can reach 60FPS with lower performance.

MethodEPE_all on sceneflow datasetEPE_all on kitti2012 datasetEPE_all on kitti2015 dataset
ours(16X multi)1.32
Reference[1]1.525

License

Installaton

Usage

Updates

To do

pretrain model

Thanks