3D Object Representations for Fine-Grained Categorization

Jonathan Krause, Michael Stark, Jia Deng, Li Fei-Fei; Proceedings of the IEEE International Conference on Computer Vision (ICCV) Workshops, 2013, pp. 554-561


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 ultrawide baseline matching and 3D reconstruction.

Related Material

author = {Jonathan Krause and Michael Stark and Jia Deng and Li Fei-Fei},
title = {3D Object Representations for Fine-Grained Categorization},
booktitle = {Proceedings of the IEEE International Conference on Computer Vision (ICCV) Workshops},
month = {June},
year = {2013}