Subspace methods for retrieval of general 3D models

Dutagaci H., Sankur B., Yemez Y.

COMPUTER VISION AND IMAGE UNDERSTANDING, vol.114, pp.865-886, 2010 (SCI-Expanded) identifier identifier

  • Publication Type: Article / Article
  • Volume: 114
  • Publication Date: 2010
  • Doi Number: 10.1016/j.cviu.2010.05.001
  • Journal Indexes: Science Citation Index Expanded (SCI-EXPANDED), Scopus
  • Page Numbers: pp.865-886
  • Keywords: 3D shape retrieval, 3D shape matching, Principal component analysis, Independent component analysis, Nonnegative matrix factorization, Distance transform, SHAPE, OBJECTS, SEARCH
  • Eskisehir Osmangazi University Affiliated: No


In statistical shape analysis, subspace methods such as PCA, ICA and NMF are commonplace, whereas they have not been adequately investigated for indexing and retrieval of generic 3D models. The main roadblock to the wider employment of these methods seems to be their sensitivity to alignment, itself an ambiguous task in the absence of common natural landmarks. We present a retrieval scheme based comparatively on three subspaces, PCA, ICA and NMF, extracted from the volumetric representations of 3D models. We find that the most propitious 3D distance transform leading to discriminative subspace features is the inverse distance transform. We mitigate the ambiguity of pose normalization with continuous PCA coupled with the use of all feasible axis labeling and reflections. The performance of the sub-space-based retrieval methods on Princeton Shape Benchmark is on a par with the state-of-the-art methods. (C) 2010 Elsevier Inc. All rights reserved.