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Object Proposals Estimation in Depth Image Using Compact 3D Shape Manifolds

  • Shuai Zheng
  • Victor Adrian Prisacariu
  • Melinos Averkiou
  • Ming-Ming Cheng
  • Niloy J. Mitra
  • Jamie Shotton
  • Philip H. S. Torr
  • Carsten Rother
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 9358)

Abstract

Man-made objects, such as chairs, often have very large shape variations, making it challenging to detect them. In this work we investigate the task of finding particular object shapes from a single depth image. We tackle this task by exploiting the inherently low dimensionality in the object shape variations, which we discover and encode as a compact shape space. Starting from any collection of 3D models, we first train a low dimensional Gaussian Process Latent Variable Shape Space. We then sample this space, effectively producing infinite amounts of shape variations, which are used for training. Additionally, to support fast and accurate inference, we improve the standard 3D object category proposal generation pipeline by applying a shallow convolutional neural network-based filtering stage. This combination leads to considerable improvements for proposal generation, in both speed and accuracy. We compare our full system to previous state-of-the-art approaches, on four different shape classes, and show a clear improvement.

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Copyright information

© Springer International Publishing Switzerland 2015

Open Access This chapter is distributed under the terms of the Creative Commons Attribution Noncommercial License, which permits any noncommercial use, distribution, and reproduction in any medium, provided the original author(s) and source are credited.

Authors and Affiliations

  • Shuai Zheng
    • 1
  • Victor Adrian Prisacariu
    • 1
  • Melinos Averkiou
    • 2
  • Ming-Ming Cheng
    • 1
    • 5
  • Niloy J. Mitra
    • 2
  • Jamie Shotton
    • 3
  • Philip H. S. Torr
    • 1
  • Carsten Rother
    • 4
  1. 1.University of OxfordOxfordUK
  2. 2.University College LondonLondonUK
  3. 3.Microsoft ResearchCambridgeUK
  4. 4.TU DresdenDresdenGermany
  5. 5.Nankai UniversityTianjinChina

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