Hao Su, Charles R. Qi, Yangyan Li and Leonidas J. Guibas, Render for CNN: Viewpoint Estimation in Images Using CNNs Trained with Rendered 3D Model Views, The 15th International Conference on Computer Vision (ICCV), Santiago, Chile, November 2015.

Abstract:

Object viewpoint estimation from 2D images is an essential task in computer vision. However, two issues hinder its progress: scarcity of training data with viewpoint annotations, and a lack of powerful features. Inspired by the growing availability of 3D models, we propose a framework to address both issues by combining render-based image synthesis and CNNs (Convolutional Neural Networks). We believe that 3D models have the potential in generating a large number of images of high variation, which can be well exploited by deep CNN with a high learning capacity. Towards this goal, we propose a scalable and overfit- resistant image synthesis pipeline, together with a novel CNN specifically tailored for the viewpoint estimation task. Experimentally, we show that the viewpoint estimation from our pipeline can significantly outperform state-of-the-art methods on PASCAL 3D+ benchmark.

Bibtex: 

@InProceedings{sqlg-rfc-15,
    Title={Render for CNN: Viewpoint Estimation in Images Using CNNs Trained with Rendered 3D Model Views},
    Author={Su, Hao and Qi, Charles R. and Li, Yangyan and Guibas, Leonidas J.},
    Booktitle={The IEEE International Conference on Computer Vision (ICCV)},
    month = {December},
    Year= {2015}
}