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GANet

GA-Net: Guided Aggregation Net for End-to-end Stereo Matching

Brief Introduction

We are formulating traditional geometric and optimization of stereo into deep neural networks ...

Oral Presentation

Slides, Video, Poster

Building Requirements:

gcc: >=5.3
GPU mem: >=7G (for testing);  >=12G (for training, >=22G is prefered)
pytorch: >=1.0
tested platform/settings:
  1) ubuntu 16.04 + cuda 10.0 + python 3.6, 3.7
  2) centos + cuda 9.2 + python 3.7

Notice:

Installing pytorch from source helps solve most of the errors (lib conflicts).

Please refer to https://github.com/pytorch/pytorch about how to reinstall pytorch from source.

How to Use?

step 1: compile the libs by "sh compile.sh"

step 2: download and prepare the dataset

download SceneFLow dataset: "FlyingThings3D", "Driving" and "Monkaa" (final pass and disparity files).

  -mv all training images (totallty 29 folders) into ${your dataset PATH}/frames_finalpass/TRAIN/
  -mv all corresponding disparity files (totallty 29 folders) into ${your dataset PATH}/disparity/TRAIN/
  -make sure the following 29 folders are included in the "${your dataset PATH}/disparity/TRAIN/" and "${your dataset PATH}/frames_finalpass/TRAIN/":
    
    15mm_focallength	35mm_focallength		A			 a_rain_of_stones_x2		B				C
    eating_camera2_x2	eating_naked_camera2_x2		eating_x2		 family_x2			flower_storm_augmented0_x2	flower_storm_augmented1_x2
    flower_storm_x2	funnyworld_augmented0_x2	funnyworld_augmented1_x2	funnyworld_camera2_augmented0_x2	funnyworld_camera2_augmented1_x2	funnyworld_camera2_x2
    funnyworld_x2	lonetree_augmented0_x2		lonetree_augmented1_x2		lonetree_difftex2_x2		  lonetree_difftex_x2		lonetree_winter_x2
    lonetree_x2		top_view_x2			treeflight_augmented0_x2	treeflight_augmented1_x2  	treeflight_x2	

download and extract kitti and kitti2015 datasets.

Step 3: revise parameter settings and run "train.sh" and "predict.sh" for training, finetuning and prediction/testing.

Pretrained models:

Pretrained models on sceneflow, kitti and kitti2015 datasets are avaiable at: (will update later)

sceneflow (for fine-tuning, only 10 epoch) kitti2012 (after fine-tuning) kitti2015 (after fine-tuning)
Google Drive Google Drive Google Drive

Results:

The results should be better than those reported in the paper.

Reference:

If you find the code useful, please cite our paper:

@inproceedings{zhang2019GANet,
  title={GA-Net: Guided Aggregation Net for End-to-end Stereo Matching},
  author={Zhang, Feihu and Prisacariu, Victor and Yang, Ruigang and Torr, Philip},
  booktitle={Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},
  year={2019}
}