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3D Surface Reconstruction by Pointillism

  • Olivia WilesEmail author
  • Andrew Zisserman
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 11131)

Abstract

The objective of this work is to infer the 3D shape of an object from a single image. We use sculptures as our training and test bed, as these have great variety in shape and appearance.

To achieve this we build on the success of multiple view geometry (MVG) which is able to accurately provide correspondences between images of 3D objects under varying viewpoint and illumination conditions, and make the following contributions: first, we introduce a new loss function that can harness image-to-image correspondences to provide a supervisory signal to train a deep network to infer a depth map. The network is trained end-to-end by differentiating through the camera. Second, we develop a processing pipeline to automatically generate a large scale multi-view set of correspondences for training the network. Finally, we demonstrate that we can indeed obtain a depth map of a novel object from a single image for a variety of sculptures with varying shape/texture, and that the network generalises at test time to new domains (e.g. synthetic images).

Notes

Acknowledgements

The authors would like to thank Fatma Guney for helpful feedback and suggestions. This work was funded by an EPSRC studentship and EPSRC Programme Grant Seebibyte EP/M013774/1.

Supplementary material

478822_1_En_21_MOESM1_ESM.pdf (3.2 mb)
Supplementary material 1 (pdf 3268 KB)

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Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  1. 1.Visual Geometry Group, Department of Engineering ScienceUniversity of OxfordOxfordUK

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