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Layer-Structured 3D Scene Inference via View Synthesis

  • Shubham TulsianiEmail author
  • Richard Tucker
  • Noah Snavely
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 11211)

Abstract

We present an approach to infer a layer-structured 3D representation of a scene from a single input image. This allows us to infer not only the depth of the visible pixels, but also to capture the texture and depth for content in the scene that is not directly visible. We overcome the challenge posed by the lack of direct supervision by instead leveraging a more naturally available multi-view supervisory signal. Our insight is to use view synthesis as a proxy task: we enforce that our representation (inferred from a single image), when rendered from a novel perspective, matches the true observed image. We present a learning framework that operationalizes this insight using a new, differentiable novel view renderer. We provide qualitative and quantitative validation of our approach in two different settings, and demonstrate that we can learn to capture the hidden aspects of a scene. The project website can be found at https://shubhtuls.github.io/lsi/.

Notes

Acknowledgments

We would like to thank Tinghui Zhou and John Flynn for helpful discussions and comments. This work was done while ST was an intern at Google.

Supplementary material

474212_1_En_19_MOESM1_ESM.pdf (1.7 mb)
Supplementary material 1 (pdf 1711 KB)

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Copyright information

© Springer Nature Switzerland AG 2018

Authors and Affiliations

  • Shubham Tulsiani
    • 1
    Email author
  • Richard Tucker
    • 2
  • Noah Snavely
    • 2
  1. 1.University of California, BerkeleyBerkeleyUSA
  2. 2.GoogleMenlo ParkUSA

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