• Corpus ID: 221970697

Towards General Purpose and Geometry Preserving Single-View Depth Estimation

  title={Towards General Purpose and Geometry Preserving Single-View Depth Estimation},
  author={Mikhail Romanov and Nikolay Patatkin and Anna Vorontsova and Anton Konushin},
Single-view depth estimation plays a crucial role in scene understanding for AR applications and 3D modelling as it allows to retrieve the geometry of a scene. However, it is only possible if the inverse depth estimates are unbiased, i.e. they are either absolute or Up-to-Scale (UTS). In recent years, great progress has been made in general-purpose single-view depth estimation. Nevertheless, the latest general-purpose models were trained using ranking or on Up-to-Shift-Scale (UTSS) data. As a… 

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