/StereoNet-ActiveStereoNet

stereo matching StereoNet: Guided Hierarchical Refinement for Real-Time Edge-Aware Depth prediction model in pytorch. ECCV2018; ActiveStereoNet:End-to-End Self-Supervised Learning for Active Stereo Systems ECCV2018 Oral

Primary LanguagePythonMIT LicenseMIT

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

ActiveStereoNet:End-to-End Self-Supervised Learning for Active Stereo Systems ECCV2018 Oral

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 25 FPS 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 on sceneflow dataset by end-to-end training. the following are the side outputs and the prediction example

train example

train example

test example

test example test example over 100FPS on titan xp gpu

large example

test example

16FPS on titan xp gpu

Citation

  • refercence[1]

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.

Method EPE_all on sceneflow dataset EPE_all on kitti2012 dataset EPE_all on kitti2015 dataset
ours(8X single) stage0:2.26 stage1:1.38
Reference[1] stage1: 1.525

License

  • Our code is released under MIT License (see LICENSE file for details).

Installaton

  • python3.6
  • pytorch0.4

Usage

  • run main8Xmulti.py

Updates

  • finetune the performance beating the original paper.

rethink

  • Do not design massive deep networks with multiple stages to improve kitti by 1%(no meaning doing this)
  • Use metrics that matter for visual navigation (hint: not L1 depth error)
  • ...

pretrain model

Thanks