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Dataset for image stitching by line-guided local warping with global similarity constraint, PR2018

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Image Stitching by Line-guided Local Warping with Global Similarity Constraint (PR2018)

A collection of image stitching datasets used for image stitching by line-guided local warping with global similarity constraint, PR, 2018.

Recent image stitching work can be found in: awesome-computational-photography.

Content

Traditional Image Stitching

SVA Dataset (2011)

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APAP Dataset (2013)

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  • Paper: As-Projective-As-Possible Image Stitching with Moving DLT, CVPR2013, TPAMI2014
  • Project: Official, Python Code, C++
  • Download: dataset
  • Details: 8 sets of images, including railtracks, temple, carpark, apartment, chess/girl, construction site, and garden.
  • Reference:
    [18] Smoothly varying affine stitching, CVPR2011.
    [22] Constructing image panoramas using dual-homography warping, CVPR2011.

Parallax-tolerant Stitching Dataset (2014)

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SPHP Dataset (2014)

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Stereostitch Dataset (2015)

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NISwGSP Dataset (2016)

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SEAGULL Dataset (2016)

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REW Dataset (2018)

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Dataset for Stitching with Multiple Registrations (2018)

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Object-Centered Stitching Dataset (2018)

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BRAS Dataset (2019)

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SPW Dataset (2020)

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VPG Dataset (2020)

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  • Paper: Vanishing Point Guided Natural Image Stitching, arXiv2020
  • Project: http://cvrs.whu.edu.cn/projects/VPGStitching/
  • Download: dataset
  • Details: The dataset contains 36 sets of images, of which 12 sets of synthetic images and 24 sets of real images. All synthetic images were generated through 3Ds Max rendering hence the associated parameters are known. All real images were captured by a mobile phone. The VPG dataset contains both indoor scenes and outdoor street-view scenes. All images were carefully collected to ensure the Manhattan assumption. The number of images involved in stitching in each set ranges from 5 to 72.

LPC Dataset (2021)

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GES-50 (2022)

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Color Consistency Dataset (2019)

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OpenPano Dataset (2016)

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  • Paper: Open-source panorama stitching program written in C++ from scratch.
  • Project: https://github.com/ppwwyyxx/OpenPano
  • Download: dataset
  • Details: It contains 8 sets of images for panorama stitching, and the number of images for each set ranges from 4 to 38.

Aerial Image Stitching (AIS) Dataset

Deep Learning Image Stitching

Hmg-dynamics (2020)

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Content-Aware-DeepH-Data (2020)

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UDIS-D (2021)

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DIR-D (2022)

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  • Paper: Deep Rectangling for Image Stitching: A Learning Baseline, CVPR2022
  • Project: https://github.com/nie-lang/DeepRectangling
  • Download: dataset
  • Details: DIR-D dataset with a wide range of irregular boundaries and scenes, which includes 5,839 samples for training and 519 samples for testing. Every image in the dataset has a resolution of 512×384. The DIR-D dataset is a synthesized dataset from the UDIS-D and MS-COCO datasets, in which each sample is a triplet consisting of a stitched image (I), a mask (M), and a rectangling label (R).

WSSN Dataset (2022)

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  • Paper: Weakly-Supervised Stitching Network for Real-World Panoramic Image Generation, ECCV2022
  • Project: https://eadcat.github.io/WSSN/
  • Download: dataset, code
  • Details: The dataset is a fisheye image dataset collected by a commercial VR camera called Kandao Obsidian R for image stitching. It can capture six fisheye images simultaneously using six lenses rotated at 60° intervals. Three fisheye images rotated by 0°, 120°, and 240° as inputs to the stitching model while the remaining three images rotated by 60°, 180°, and 300° are utilized as weak supervisions. In this dataset, 47,063 sets of images are used for the training and 1,400 for the test. Each training set includes three input fisheye images, three ERP images for weak supervision, and three masks.

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Dataset for image stitching by line-guided local warping with global similarity constraint, PR2018

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