"depth prediction"の検索結果1 - 5 件 / 5件

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  Moving Camera, Moving People: A Deep Learning Approach to Depth Prediction

  • 16 users
  • ai.googleblog.com
  • テクノロジー
  • 2019/05/24

  Philosophy We strive to create an environment conducive to many different types of research across many different time scales and levels of risk. Learn more about our Philosophy Learn more

  • AI
  • google
  • research
  • camera
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  CNN-SLAM: Real-time dense monocular SLAM with learned depth prediction

  • 5 users
  • www.youtube.com
  • テクノロジー
  • 2017/05/14

  "CNN-SLAM: Real-time dense monocular SLAM with learned depth prediction," K. Tateno, F. Tombari, I. Laina, N. Navab, IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR), 2017. http://campar.in.tum.de/pub/tateno2017cvpr/tateno2017cvpr.pdf Given the recent advances in depth prediction from Convolutional Neural Networks (CNNs), this paper investigates how predicted dep

  
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  CNN-SLAM: Real-time dense monocular SLAM with learned depth prediction

  • 4 users
  • arxiv.org
  • 学び
  • 2017/05/14

  Given the recent advances in depth prediction from Convolutional Neural Networks (CNNs), this paper investigates how predicted depth maps from a deep neural network can be deployed for accurate and dense monocular reconstruction. We propose a method where CNN-predicted dense depth maps are naturally fused together with depth measurements obtained from direct monocular SLAM. Our fusion scheme privi
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  GitHub - mrharicot/monodepth: Unsupervised single image depth prediction with CNNs

  • 3 users
  • github.com/mrharicot
  • テクノロジー
  • 2018/06/29

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  MegaDepth: Learning Single-View Depth Prediction from Internet Photos

  • 3 users
  • www.cs.cornell.edu
  • テクノロジー
  • 2019/02/14

  MegaDepth: Learning Single-View Depth Prediction from Internet Photos We use large Internet image collections, combined with 3D reconstruction and semantic labeling methods, to generate large amounts of training data for single-view depth prediction. (a), (b), (e): Example input RGB images. (c), (d), (f): Depth maps predicted by our MegaDepth-trained CNN (blue=near, red=far). For these results, th