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Efficient Single-View 3D Co-segmentation Using Shape Similarity and Spatial Part Relations

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Pattern Recognition (GCPR 2016)

Part of the book series: Lecture Notes in Computer Science ((LNIP,volume 9796))

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Abstract

The practical use of the latest methods for supervised 3D shape co-segmentation is limited by the requirement of diverse training data and a watertight mesh representation. Driven by practical considerations, we assume only one reference shape to be available and the query shape to be provided as a partially visible point cloud. We propose a novel co-segmentation approach that constructs a part-based object representation comprised of shape appearance models of individual parts and isometric spatial relations between the parts. The partial query shape is pre-segmented using planar cuts, and the segments accompanied by the learned representation induce a compact Conditional Random Field (CRF). CRF inference is performed efficiently by \(A^*\)-search with global optimality guarantees. A comparative evaluation with two baselines on partial views generated from the Labelled Princeton Segmentation Benchmark and point clouds recorded with an RGB-D sensor demonstrate superiority of the proposed approach both in accuracy and efficiency.

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Notes

  1. 1.

    http://www.ais.uni-bonn.de/data/alroma.

  2. 2.

    Reported as one minus Rand index, by convention.

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Acknowledgements

This work was supported by German Research Foundation (DFG) under grant BE 2556/12 ALROMA in priority programme SPP 1527 Autonomous Learning.

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Authors and Affiliations

  1. Computer Science Institute, University of Bonn, Bonn, Germany

    Nikita Araslanov, Seongyong Koo, Juergen Gall & Sven Behnke

Authors
  1. Nikita Araslanov
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  2. Seongyong Koo
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  4. Sven Behnke
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Correspondence to Nikita Araslanov .

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Editors and Affiliations

  1. University of Hannover, Hannover, Germany

    Bodo Rosenhahn

  2. Max Planck Institute for Informatics, Saarbrücken, Germany

    Bjoern Andres

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Araslanov, N., Koo, S., Gall, J., Behnke, S. (2016). Efficient Single-View 3D Co-segmentation Using Shape Similarity and Spatial Part Relations. In: Rosenhahn, B., Andres, B. (eds) Pattern Recognition. GCPR 2016. Lecture Notes in Computer Science(), vol 9796. Springer, Cham. https://doi.org/10.1007/978-3-319-45886-1_24

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  • DOI: https://doi.org/10.1007/978-3-319-45886-1_24

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