Cost Volume Refinement for Depth Prediction

Joao Afonso Cardoso, Nuno Goncalves, Michael Wimmer
Cost Volume Refinement for Depth Prediction
In Proceedings of the 25th International Conference on Pattern Recognition, pages 1-8. January 2021.

Information

Abstract

Light-field cameras are becoming more popular in the consumer market. Their data redundancy allows, in theory, to accurately refocus images after acquisition and to predict the depth of each point visible from the camera. Combined, these two features allow for the generation of full-focus images, which is impossible in traditional cameras. Multiple methods for depth prediction from light fields (or stereo) have been proposed over the years. A large subset of these methods relies on cost-volume estimates – 3D objects where each layer represents a heuristic of whether each point in the image is at a certain distance from the camera. Generally, this volume is used to regress a depth map, which is then refined for better results. In this paper, we argue that refining the cost volumes is superior to refining the depth maps in order to further increase the accuracy of depth predictions. We propose a set of cost-volume refinement algorithms and show their effectiveness.

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BibTeX

@inproceedings{cardoso-2021-cost,
  title =      "Cost Volume Refinement for Depth Prediction",
  author =     "Joao Afonso Cardoso and Nuno Goncalves and Michael Wimmer",
  year =       "2021",
  abstract =   "Light-field cameras are becoming more popular in the
               consumer market. Their data redundancy allows, in theory, to
               accurately refocus images after acquisition and to predict
               the depth of each point visible from the camera. Combined,
               these two features allow for the generation of full-focus
               images, which is impossible in traditional cameras. Multiple
               methods for depth prediction from light fields (or stereo)
               have been proposed over the years. A large subset of these
               methods relies on cost-volume estimates – 3D objects where
               each layer represents a heuristic of whether each point in
               the image is at a certain distance from the camera.
               Generally, this volume is used to regress a depth map, which
               is then refined for better results. In this paper, we argue
               that refining the cost volumes is superior to refining the
               depth maps in order to further increase the accuracy of
               depth predictions. We propose a set of cost-volume
               refinement algorithms and show their effectiveness.",
  month =      jan,
  booktitle =  "Proceedings of the 25th International Conference on Pattern
               Recognition",
  doi =        "978-1-7281-8808-9",
  event =      "25th International Conference on Pattern Recognition",
  pages =      "1--8",
  keywords =   "depth reconstruction, light fields, cost volumes",
  URL =        "https://www.cg.tuwien.ac.at/research/publications/2021/cardoso-2021-cost/",
}