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A Contour Completion Model for Augmenting Surface Reconstructions

  • Nathan Silberman
  • Lior Shapira
  • Ran Gal
  • Pushmeet Kohli
Part of the Lecture Notes in Computer Science book series (LNCS, volume 8691)

Abstract

The availability of commodity depth sensors such as Kinect has enabled development of methods which can densely reconstruct arbitrary scenes. While the results of these methods are accurate and visually appealing, they are quite often incomplete. This is either due to the fact that only part of the space was visible during the data capture process or due to the surfaces being occluded by other objects in the scene. In this paper, we address the problem of completing and refining such reconstructions. We propose a method for scene completion that can infer the layout of the complete room and the full extent of partially occluded objects. We propose a new probabilistic model, Contour Completion Random Fields, that allows us to complete the boundaries of occluded surfaces. We evaluate our method on synthetic and real world reconstructions of 3D scenes and show that it quantitatively and qualitatively outperforms standard methods. We created a large dataset of partial and complete reconstructions which we will make available to the community as a benchmark for the scene completion task. Finally, we demonstrate the practical utility of our algorithm via an augmented-reality application where objects interact with the completed reconstructions inferred by our method.

Keywords

3D Reconstruction Scene Completion Surface Reconstruction Contour Completion 

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Copyright information

© Springer International Publishing Switzerland 2014

Authors and Affiliations

  • Nathan Silberman
    • 1
  • Lior Shapira
    • 2
  • Ran Gal
    • 2
  • Pushmeet Kohli
    • 3
  1. 1.Courant InstituteNew York UniversityUSA
  2. 2.Microsoft ResearchUSA
  3. 3.Microsoft ResearchCambridgeUK

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