Robust region detection via consensus segmentation of deformable shapes

E. Rodolà, S. Rota Bulò, D. Cremers

Research output: Contribution to journalArticlepeer-review

30 Scopus citations


We consider the problem of stable region detection and segmentation of deformable shapes. We pursue this goal by determining a consensus segmentation from a heterogeneous ensemble of putative segmentations, which are generated by a clustering process on an intrinsic embedding of the shape. The intuition is that the consensus segmentation, which relies on aggregate statistics gathered from the segmentations in the ensemble, can reveal components in the shape that are more stable to deformations than the single baseline segmentations. Compared to the existing approaches, our solution exhibits higher robustness and repeatability throughout a wide spectrum of non-rigid transformations. It is computationally efficient, naturally extendible to point clouds, and remains semantically stable even across different object classes. A quantitative evaluation on standard datasets confirms the potentiality of our method as a valid tool for deformable shape analysis.

Original languageEnglish
Pages (from-to)97-106
Number of pages10
JournalComputer Graphics Forum
Issue number5
StatePublished - Aug 2014


  • Categories and Subject Descriptors (according to ACM CCS)
  • I.3.5 [Computer Graphics]: Computational Geometry and Object Modeling - Shape Analysis


Dive into the research topics of 'Robust region detection via consensus segmentation of deformable shapes'. Together they form a unique fingerprint.

Cite this