Jointly Optimizing 3D Model Fitting and Fine-Grained Classification
Abstract
3D object modeling and fine-grained classification are often treated as separate tasks. We propose to optimize 3D model fitting and fine-grained classification jointly. Detailed 3D object representations encode more information (e.g., precise part locations and viewpoint) than traditional 2D-based approaches, and can therefore improve fine-grained classification performance. Meanwhile, the predicted class label can also improve 3D model fitting accuracy, e.g., by providing more detailed class-specific shape models. We evaluate our method on a new fine-grained 3D car dataset (FG3DCar), demonstrating our method outperforms several state-of-the-art approaches. Furthermore, we also conduct a series of analyses to explore the dependence between fine-grained classification performance and 3D models.
Cite
Text
Lin et al. "Jointly Optimizing 3D Model Fitting and Fine-Grained Classification." European Conference on Computer Vision, 2014. doi:10.1007/978-3-319-10593-2_31Markdown
[Lin et al. "Jointly Optimizing 3D Model Fitting and Fine-Grained Classification." European Conference on Computer Vision, 2014.](https://mlanthology.org/eccv/2014/lin2014eccv-jointly/) doi:10.1007/978-3-319-10593-2_31BibTeX
@inproceedings{lin2014eccv-jointly,
title = {{Jointly Optimizing 3D Model Fitting and Fine-Grained Classification}},
author = {Lin, Yen-Liang and Morariu, Vlad I. and Hsu, Winston H. and Davis, Larry S.},
booktitle = {European Conference on Computer Vision},
year = {2014},
pages = {466-480},
doi = {10.1007/978-3-319-10593-2_31},
url = {https://mlanthology.org/eccv/2014/lin2014eccv-jointly/}
}