Stacked Recurrent Hourglass Network for Stereo Matching

Last update: Jan 03, 2023

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Overview

SRH-Net: Stacked Recurrent Hourglass

Introduction

This repository is supplementary material of our RA-L submission, which helps reviewers to understand and evaluate the submitted paper. The final version will be released to the community in the future.

For commercial purposes, please contact the authors: [email protected]. If you use PlanarSLAM in an academic work, please cite:

inproceedings{dusrhnet,
  author = {Hongzhi Du, Yanyan Li, Yanbiao Sun, Jigui Zhu and Federico Tombari},
  title = {SRH-Net: Stacked Recurrent Hourglass Network for Stereo Matching},
  year = {2021},
  booktitle = {arXiv preprint arXiv:2105.11587},
 }

Installation

We suggest to create an Anaconda environment and install the dependencies:

conda create -y -n SRHNET python=3.6
conda activate SRHNET
pip install -r requirements.txt

Evaluation on the public datasets

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

  -mv all training images (totallty 29 folders) into ${your dataset PATH}/frames_cleanpass/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_cleanpass/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.

Evaluation and Prediction

Revise parameter settings and run "myevalution.sh" and "predict.sh" for evaluation and prediction on the SceneFLow dataset and KITTI datasets. Note that the “crop_width” and “crop_height” must be multiple of 16, "max_disp" must be multiple of 4 (default: 192).

Test on your own stereo images

The repo provides the pretrained model for testing. Please extract the .zip file into SRHNet Folder and use the following command to test your stereo images.

python test_img.py --crop_height= image height\
                   --crop_width= image width\
                   --max_disp=192\
                   --leftimg='path/to/left/image'\
                   --rightimg='path/to/left/image'\
                   --resume='path/to/pretrained/model'

As an example, we also provide stereo images that can be tested by using the following command,

python test_img.py --crop_height=384\
                   --crop_width=1248\
                   --max_disp=192\
                   --leftimg='./demo/left12_10.png'\
                   --rightimg='./demo/right12_10.png'\
                   --resume='./finetune2_kitti2015_epoch_8.pth'

Stacked Recurrent Hourglass Network for Stereo Matching

Related tags

Overview

SRH-Net: Stacked Recurrent Hourglass

Introduction

Installation

Evaluation on the public datasets

Evaluation and Prediction

Test on your own stereo images

Owner

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