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Unstructured Multi-view Depth Estimation Using Mask-Based Multiplane Representation

  • Yuxin HouEmail author
  • Arno Solin
  • Juho Kannala
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 11482)

Abstract

This paper presents a novel method, MaskMVS, to solve depth estimation for unstructured multi-view image-pose pairs. In the plane-sweep procedure, the depth planes are sampled by histogram matching that ensures covering the depth range of interest. Unlike other plane-sweep methods, we do not rely on a cost metric to explicitly build the cost volume, but instead infer a multiplane mask representation which regularizes the learning. Compared to many previous approaches, we show that our method is lightweight and generalizes well without requiring excessive training. We outperform the current state-of-the-art and show results on the sun3d, scenes11, MVS, and RGBD test data sets.

Keywords

Computer vision Depth estimation Multi-view stereo 

Notes

Acknowledgements

We acknowledge computing resources by Aalto Science-IT and CSC, and funding from the Academy of Finland (308640 and 277685).

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Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  1. 1.Department of Computer ScienceAalto UniversityEspooFinland

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