Transductive 3D shape segmentation using sparse reconstruction

  • Xu W
  • Shi Z
  • Xu M
 et al. 
  • 1

    Readers

    Mendeley users who have this article in their library.
  • N/A

    Citations

    Citations of this article.

Abstract

© 2012 The Author(s). We propose a transductive shape segmentation algorithm, which can transfer prior segmentation results in database to new shapes without explicitly specification of prior category information. Our method first partitions an input shape into a set of segmentations as a data preparation, and then a linear integer programming algorithm is used to select segments from them to form the final optimal segmentation. The key idea is to maximize the segment similarity between the segments in the input shape and the segments in database, where the segment similarity is computed through sparse reconstruction error. The segment-level similarity enables to handle a large amount of shapes with significant topology or shape variations with a small set of segmented example shapes. Experimental results show that our algorithm can generate high quality segmentation and semantic labeling results in the Princeton segmentation benchmark.

Get free article suggestions today

Mendeley saves you time finding and organizing research

Sign up here
Already have an account ?Sign in

Find this document

Authors

  • W. Xu

  • Z. Shi

  • M. Xu

  • K. Zhou

  • J. Wang

  • B. Zhou

Cite this document

Choose a citation style from the tabs below

Save time finding and organizing research with Mendeley

Sign up for free