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Image-based localization using LSTMs for structured feature correlation

F. Walch, C. Hazirbas, L. Leal-Taixé, T. Sattler, S. Hilsenbeck, D. Cremers ,ICCV, 2017.


  1. propose a new CNN+LSTM architecture for camera pose regression in indoor and outdoor scenes.

    • CNN architecture: feature extraction
      • Leverage Places pretrained GoogLeNet
      • Loss function the same as PoseNet
    • Structured feature correlation with LSTMs
  2. Extensive quantitative comparison of CNN-based and SIFT-based localization methods:

    • classic SIFT-based methods still outperform all CNN-based methods.
    • CNN-based method can handle such a challenging scenario while SIFT-based methods fail completely.
  3. A new challenging large indoor dataset TUM-LSI:

    • exhibiting repetitive structures and weakly textured surfaces, and provide accurate ground truth poses.

Strengths / Novelties

  1. LSTM-based structured feature correlation can lead to drastic improvements in localization performance compared to other CNN-based methods.
  2. Succeeds in a very challenging scenario where SIFT-based methods fail.
  3. Exploring CNN-based localization in hard scenarios is a promising research direction.