Dae-Young Song1 · Geonsoo Lee1 · HeeKyung Lee2 · Gi-Mun Um2 · Donghyeon Cho1
1 Chungnam National University · 2 Electronics and Telecommunications Research Institute (ETRI)
An Official PyTorch Implementation of Weakly-Supervised Stitching Network for Real-World Panoramic Image Generation.
conda create -n wssn python=3.8
conda activate wssn
sh env.sh
sh download.sh
or download the files below manually.
Dataset for Demo: Google Drive (651M)
Network Checkpoints: Google Drive (3.9G)
# If you want to use your own GPU, set the following options:
# Example 1: --gpu 0 1 2 3 --world-size 4 --npgpu 4 (DDP)
# Example 2: --gpu 2 3 --world-size 2 --npgpu 4 (DDP)
# Example 3: --gpu 0 (Single GPU)
# Check "options.py" for more details.
# All models below use single homography for one input.
# The default setting for the shell scripts below are 'CPU'.
# 01. Final Model
sh scripts/test-final.sh
# 02. Global Homography Only (W/O Local Adj.)
sh scripts/test-homography.sh
# 03. Without Color Correction
(https://ieeexplore.ieee.org/document/9393563)
sh scripts/test-spl.sh
# 04. Pre-color Correction (W/O Post-Color Correction)
sh scripts/test-pre.sh
# 05. Post-color Correction (W/O Pre-Color Correction)
sh scripts/test-post.sh
# 06. Final Model Trained with L1(Pixel-wise) Loss
sh scripts/test-L1.sh
@InProceedings{Song2022Weakly,
author={Song, Dae-Young and Lee, Geonsoo and Lee, HeeKyung and Um, Gi-Mun and Cho, Donghyeon},
title={Weakly-Supervised Stitching Network for Real-World Panoramic Image Generation},
journal={European Conference on Computer Vision (ECCV)},
pages={54--71},
year={2022},
organization={Springer}
}
@article{song2021end,
title={End-to-End Image Stitching Network via Multi-Homography Estimation},
author={Song, Dae-Young and Um, Gi-Mun and Lee, Hee Kyung and Cho, Donghyeon},
journal={IEEE Signal Processing Letters (SPL)},
volume={28},
pages={763--767},
year={2021},
publisher={IEEE}
}
2022-11-01 Fix
- PyTorch version issue -> 1.12.1 / cudatoolkit -> 11.3
- Download shell script
- checkpoint loading device
- redundant normalization of the color-correction
Data dual License - CC BY-NC-ND 4.0, Commercial License
Source dual License - BSD-3-Clause License, Commercial License
Question: [email protected]; [email protected]
License: [email protected]
If you want to use and/or redistribute this source commercially, please consult [email protected] for details in advance.