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https://github.com/uber-research/DeepPruner
DeepPruner: Learning Efficient Stereo Matching via Differentiable PatchMatch (ICCV 2019)
https://github.com/uber-research/DeepPruner
iccv2019 patchmatch pytorch real-time stereo-matching stereo-vision
Last synced: 11 days ago
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DeepPruner: Learning Efficient Stereo Matching via Differentiable PatchMatch (ICCV 2019)
- Host: GitHub
- URL: https://github.com/uber-research/DeepPruner
- Owner: uber-research
- License: other
- Created: 2019-10-15T23:48:59.000Z (about 5 years ago)
- Default Branch: master
- Last Pushed: 2020-09-05T08:17:08.000Z (about 4 years ago)
- Last Synced: 2024-08-01T20:36:55.836Z (3 months ago)
- Topics: iccv2019, patchmatch, pytorch, real-time, stereo-matching, stereo-vision
- Language: Python
- Homepage:
- Size: 19.3 MB
- Stars: 349
- Watchers: 17
- Forks: 41
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
- License: LICENSE
Awesome Lists containing this project
- awesome-computer-vision-papers - 2019ICCV
README
# DeepPruner: Learning Efficient Stereo Matching via Differentiable PatchMatch
This repository releases code for our paper [DeepPruner: Learning Efficient Stereo Matching via Differentiable PatchMatch](https://arxiv.org/abs/1909.05845).
##### Table of Contents
[DeepPruner](#DeepPruner)
[Differentiable Patch Match](#DifferentiablePatchMatch)
[Requirements (Major Dependencies)](#Requirements)
[Citation](#Citation)+ An efficient "Real Time Stereo Matching" algorithm, which takes as input 2 images and outputs a disparity (or depth) map.
![](readme_images/DeepPruner.png)
+ Results/ Metrics:
+ [**KITTI**](http://www.cvlibs.net/datasets/kitti/eval_scene_flow.php?benchmark=stereo): **Results competitive to SOTA, while being real-time (8x faster than SOTA). SOTA among published real-time algorithms**.
![](readme_images/KITTI_test_set.png)
![](readme_images/CRP.png)
![](readme_images/uncertainty_vis.png)
+ [**ETH3D**](https://www.eth3d.net/low_res_two_view?mask=all&metric=bad-2-0): **SOTA among all ROB entries**.
+ **SceneFlow**: **2nd among all published algorithms, while being 8x faster than the 1st.**
+ [**Robust Vision Challenge**](http://www.robustvision.net/index.php): **Overall ranking 1st**.
+ Runtime: **62ms** (for DeepPruner-fast), **180ms** (for DeepPruner-best)
+ Cuda Memory Requirements: **805MB** (for DeepPruner-best)
### **Differentiable Patch Match**
+ Fast algorithm for finding dense nearest neighbor correspondences between patches of images regions.
Differentiable version of the generalized Patch Match algorithm. ([Barnes et al.](https://gfx.cs.princeton.edu/pubs/Barnes_2010_TGP/index.php))
More details in the corresponding folder README.
## Requirements (Major Dependencies)
+ Pytorch (0.4.1+)
+ Python2.7
+ torchvision (0.2.0+)If you use our source code, or our paper, please consider citing the following:
> @inproceedings{Duggal2019ICCV,
title = {DeepPruner: Learning Efficient Stereo Matching via Differentiable PatchMatch},
author = {Shivam Duggal and Shenlong Wang and Wei-Chiu Ma and Rui Hu and Raquel Urtasun},
booktitle = {ICCV},
year = {2019}
}Correspondences to Shivam Duggal , Shenlong Wang , Wei-Chiu Ma