StereoNet: Guided Hierarchical Refinement for Real-Time Edge-Aware Depth prediction model in pytorch. ECCV2018
I will release all the code after I get the best result.
If you want to communicate with me about the StereoNet, please concact me without hesitating. My email:
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
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} }
I implement the real-time stereo model according to the XXX model in pytorch
| Method | EPE_all on sceneflow dataset | EPE_all on kitti2012 dataset | EPE_all on kitti2015 dataset |
|---|---|---|---|
| ours(16X multi) | 1.32 | *** | *** |
| Reference[1] | 1.525 | *** | *** |