State of the Art on 3D Reconstruction with RGB-D Cameras

Michael Zollhöfer, Patrick Stotko, Andreas Görlitz, Christian Theobalt, Matthias Nießner, Reinhard Klein und Andreas Kolb
In: Computer Graphics Forum (Eurographics State of the Art Reports) (2018), 37:2(625-652)
 

Abstract

The advent of affordable consumer grade RGB-D cameras has brought about a profound advancement of visual scene reconstruction methods. Both computer graphics and computer vision researchers spend significant effort to develop entirely new algorithms to capture comprehensive shape models of static and dynamic scenes with RGB-D cameras. This led to significant advances of the state of the art along several dimensions. Some methods achieve very high reconstruction detail, despite limited sensor resolution. Others even achieve real-time performance, yet possibly at lower quality. New concepts were developed to capture scenes at larger spatial and temporal extent. Other recent algorithms flank shape reconstruction with concurrent material and lighting estimation, even in general scenes and unconstrained conditions. In this state-of-the-art report, we analyze these recent developments in RGB-D scene reconstruction in detail and review essential related work. We explain, compare,and critically analyze the common underlying algorithmic concepts that enabled these recent advancements. Furthermore, we show how algorithms are designed to best exploit the benefits of RGB-D data while suppressing their often non-trivial data distortions. In addition, this report identifies and discusses important open research questions and suggests relevant directions for future work.

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Bibtex

@ARTICLE{Zollhoefer2018state,
    author = {Zollh{\"o}fer, Michael and Stotko, Patrick and G{\"o}rlitz, Andreas and Theobalt, Christian and Nie{\ss}ner,
              Matthias and Klein, Reinhard and Kolb, Andreas},
     pages = {625--652},
     title = {State of the Art on 3D Reconstruction with RGB-D Cameras},
   journal = {Computer Graphics Forum (Eurographics State of the Art Reports)},
    volume = {37},
    number = {2},
      year = {2018},
  abstract = {The advent of affordable consumer grade RGB-D cameras has brought about a profound advancement of
              visual scene reconstruction methods. Both computer graphics and computer vision researchers spend
              significant effort to develop entirely new algorithms to capture comprehensive shape models of
              static and dynamic scenes with RGB-D cameras. This led to significant advances of the state of the
              art along several dimensions. Some methods achieve very high reconstruction detail, despite limited
              sensor resolution. Others even achieve real-time performance, yet possibly at lower quality. New
              concepts were developed to capture scenes at larger spatial and temporal extent. Other recent
              algorithms flank shape reconstruction with concurrent material and lighting estimation, even in
              general scenes and unconstrained conditions. In this state-of-the-art report, we analyze these
              recent developments in RGB-D scene reconstruction in detail and review essential related work. We
              explain, compare,and critically analyze the common underlying algorithmic concepts that enabled
              these recent advancements. Furthermore, we show how algorithms are designed to best exploit the
              benefits of RGB-D data while suppressing their often non-trivial data distortions. In addition, this
              report identifies and discusses important open research questions and suggests relevant directions
              for future work.},
       doi = {10.1111/cgf.13386}
}