3D object representations for fine-grained categorization

Jonathan Krause, Michael Stark, Jia Deng, Li Fei-Fei

Research output: Chapter in Book/Report/Conference proceedingConference contribution

2153 Scopus citations


While 3D object representations are being revived in the context of multi-view object class detection and scene understanding, they have not yet attained wide-spread use in fine-grained categorization. State-of-the-art approaches achieve remarkable performance when training data is plentiful, but they are typically tied to flat, 2D representations that model objects as a collection of unconnected views, limiting their ability to generalize across viewpoints. In this paper, we therefore lift two state-of-the-art 2D object representations to 3D, on the level of both local feature appearance and location. In extensive experiments on existing and newly proposed datasets, we show our 3D object representations outperform their state-of-the-art 2D counterparts for fine-grained categorization and demonstrate their efficacy for estimating 3D geometry from images via ultra-wide baseline matching and 3D reconstruction.

Original languageEnglish (US)
Title of host publicationProceedings - 2013 IEEE International Conference on Computer Vision Workshops, ICCVW 2013
PublisherInstitute of Electrical and Electronics Engineers Inc.
Number of pages8
ISBN (Print)9781479930227
StatePublished - 2013
Externally publishedYes
Event2013 14th IEEE International Conference on Computer Vision Workshops, ICCVW 2013 - Sydney, NSW, Australia
Duration: Dec 1 2013Dec 8 2013

Publication series

NameProceedings of the IEEE International Conference on Computer Vision


Other2013 14th IEEE International Conference on Computer Vision Workshops, ICCVW 2013
CitySydney, NSW

All Science Journal Classification (ASJC) codes

  • Software
  • Computer Vision and Pattern Recognition


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