计算机视觉方向数据集汇总(更新中)

建筑物提取数据集

  1. Aerial Image Segmentation Dataset
    https://zenodo.org/record/1154821#.XH6HtygzbIU
  2. INRIA aerial image dataset
    https://project.inria.fr/aerialimagelabeling/files/
  3. WHU Building Dataset
    https://study.rsgis.whu.edu.cn/pages/download/building_dataset.html
  4. Massachusetts Buildings Dataset
    https://www.cs.toronto.edu/~vmnih/data/
    https://www.kaggle.com/balraj98/massachusetts-buildings-dataset
  5. 2020数字中国创新大赛—应用赛1:建筑智能普查
    https://tianchi.aliyun.com/competition/entrance/231767/information 天池比赛已结束
  6. 亚马逊SpaceNet Buildings Dataset
    https://oldpan.me/archives/download-aws-spacenet-dataset
  7. AIRS
    https://www.airs-dataset.com/

遥感影像数据集汇总https://aistudio.baidu.com/aistudio/projectdetail/882508

chip classification、Semantic Segmentation(buildings / roads)、Object Detection相关深度学习框架----rastervision

https://docs.rastervision.io/en/0.13/quickstart.html

双目深度立体匹配数据集

  1. KITTI
    A. Geiger, P. Lenz, and R. Urtasun. Are we ready for autonomous driving? the kitti vision benchmark suite. In CVPR, 2012. 2, 4. https://gas.graviti.cn/dataset/hello-dataset/KITTIStereo2015/download http://www.cvlibs.net/datasets/kitti/

  2. Flyingthings3D
    https://lmb.informatik.uni-freiburg.de/resources/datasets/SceneFlowDatasets.en.html
    ————————————————

立体匹配方向论文综述
无监督:

  1. Unsupervised Monocular Depth Estimation with Left-Right Consistency
  2. https://arxiv.org/abs/1806.01260 monodepthV2
  3. https://paperswithcode.com/paper/unsupervised-cross-spectral-stereo-matching
  4. https://paperswithcode.com/paper/towards-real-time-unsupervised-monocular on cpu
  5. Bilateral Cyclic Constraint and Adaptive Regularization for Unsupervised Monocular Depth Prediction https://arxiv.org/pdf/1903.07309.pdf
  6. SharinGAN: Combining Synthetic and Real Data for Unsupervised Geometry Estimation https://arxiv.org/pdf/2006.04026v1.pdf
  7. GCNDepth: Self-supervised Monocular Depth Estimation based on Graph Convolutional Network https://arxiv.org/pdf/2112.06782v1.pdf

  1. Unsupervised Learning of Monocular Depth Estimation and Visual Odometry with Deep Feature Reconstruction https://github.com/Huangying-Zhan/Depth-VO-Feat caffe
  2. https://openaccess.thecvf.com/content_CVPR_2020/papers/Spencer_DeFeat-Net_General_Monocular_Depth_via_Simultaneous_Unsupervised_Representation_Learning_CVPR_2020_paper.pdf
  3. Unsupervised Scale-consistent Depth and Ego-motion Learning from Monocular Video https://gitee.com/sdalxn/SC-SfMLearner-Release?_from=gitee_search
  4. Unsupervised Learning of Depth and Ego-Motion from Video https://openaccess.thecvf.com/content_cvpr_2017/papers/Zhou_Unsupervised_Learning_of_CVPR_2017_paper.pdf

  1. Learning Stereo from Single Images https://github.com/nianticlabs/stereo-from-mono GT
  2. Hierarchical Discrete Distribution Decomposition for Match Density Estimation https://github.com/ucbdrive/hd3 GT
  3. https://paperswithcode.com/paper/high-quality-monocular-depth-estimation-via 迁移学习
  4. HITNet: Hierarchical Iterative Tile Refinement Network for Real-time Stereo Matching https://github.com/meteorshowers/X-StereoLab
  5. MobileStereoNet: Towards Lightweight Deep Networks for Stereo Matching https://github.com/cogsys-tuebingen/mobilestereonet
  6. https://github.com/paprrik/awesome-Monocular-Depth-Estimation
    半监督:
  7. https://paperswithcode.com/paper/geometry-meets-semantics-for-semi-supervised

文档文种分类数据集(script identification)

  1. Chinese Ethnic Ancient Handwritten Books database, CEAHB2021-5
    https://pan.baidu.com/s/1UoeCe23YvM9ciZEpGTt-mw, Extraction code:CNAH. https://github.com/yddcode/CEAHB2021-5. 少数民族古籍ancient books 藏 纳西 傣 水 彝族

文档图像二值化数据集

  1. [DIBCO] https://vc.ee.duth.gr/dibco2019/
  2. [Persian] http://www.iapr-tc11.org/mediawiki/index.php/Persian_Heritage_Image_Binarization_Dataset_(PHIBD_2012)
    [PLM] http://amadi.univ-lr.fr/ICFHR2016_Contest/index.php/download-123
  3. 德宏傣文古籍图像二值化数据集Tai Le historical document image binarization dataset, TLHDIBD2021. https://github.com/yddcode/TLHDIBD2021
    https://pan.baidu.com/s/1Pnq1XrBM16uhavSETK8wpQ 提取码:TLDI
  4. [S-MS] http://tc11.cvc.uab.es/datasets/SMADI_1
  5. [LRDE-DBD] https://www.lrde.epita.fr/dload/olena/datasets/dbd/1.0/
  6. [Label] https://www.kist-europe.de/portal/main/main.do

古籍相关任务:
https://arxiv.org/pdf/2107.06777.pdf
https://arxiv.org/pdf/2106.06786.pdf
https://arxiv.org/pdf/2105.15093.pdf
https://arxiv.org/pdf/2102.11838.pdf

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