Shape segmentation by approximate convexity analysis

Oliver Van Kaick, Noa Fish, Yanir Kleiman, Shmuel Asafi, Daniel Cohen-Or

Research output: Contribution to journalArticlepeer-review

Abstract

We present a shape segmentation method for complete and incomplete shapes. The key idea is to directly optimize the decomposition based on a characterization of the expected geometry of a part in a shape. Rather than setting the number of parts in advance, we search for the smallest number of parts that admit the geometric characterization of the parts. The segmentation is based on an intermediate-level analysis, where first the shape is decomposed into approximate convex components, which are then merged into consistent parts based on a nonlocal geometric signature. Our method is designedto handle incomplete shapes, represented by point clouds.We show segmentation results on shapes acquired by a range scanner, and an analysis of the robustness of our method to missing regions. Moreover, our method yields results that are comparable to state-of-the-art techniques evaluated on complete shapes.

Original languageEnglish
JournalACM Transactions on Graphics
Volume34
Issue number1
DOIs
StatePublished - 29 Dec 2014

Keywords

  • Incomplete shapes
  • Missing data
  • Part characterization
  • Point clouds
  • Shape segmentation
  • Weakly convex decomposition

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