From Virtual to Reality: Fast Adaptation of Virtual Object Detectors to Real Domains
In Proceedings British Machine Vision Conference 2014
http://dx.doi.org/10.5244/C.28.82
Abstract
The most successful 2D object detection methods require a large number of images annotated with object bounding boxes to be collected for training. We present an alternative approach that trains on virtual data rendered from 3D models, avoiding the need for manual labeling. Growing demand for virtual reality applications is quickly bringing about an abundance of available 3D models for a large variety of object categories. While mainstream use of 3D models in vision has focused on predicting the 3D pose of objects, we investigate the use of such freely available 3D models for multicategory 2D object detection. To address the issue of dataset bias that arises from training on virtual data and testing on real images, we propose a simple and fast adaptation approach based on decorrelated features. We also compare two kinds of virtual data, one rendered with real-image textures and one without. Evaluation on a benchmark domain adaptation dataset demonstrates that our method performs comparably to existing methods trained on large-scale real image domains.
Session
Poster Session
Files
Extended Abstract (PDF, 1 page, 279K)Paper (PDF, 12 pages, 1.3M)
Bibtex File
Citation
Baochen Sun, and Kate Saenko. From Virtual to Reality: Fast Adaptation of Virtual Object Detectors to Real Domains. Proceedings of the British Machine Vision Conference. BMVA Press, September 2014.
BibTex
@inproceedings{BMVC.28.82
title = {From Virtual to Reality: Fast Adaptation of Virtual Object Detectors to Real Domains},
author = {Sun, Baochen and Saenko, Kate},
year = {2014},
booktitle = {Proceedings of the British Machine Vision Conference},
publisher = {BMVA Press},
editors = {Valstar, Michel and French, Andrew and Pridmore, Tony}
doi = { http://dx.doi.org/10.5244/C.28.82 }
}