Making Specific Features Less Discriminative to Improve Point-Based 3D Object Recognition
Abstract
We present a framework that retains ambiguity in feature matching to increase the performance of 3D object recognition systems. Whereas previous systems removed ambiguous correspondences during matching, we show that ambiguity should be resolved during hypothesis testing and not at the matching phase. To preserve ambiguity during matching, we vector quantize and match model features in a hierarchical manner. This matching technique allows our system to be more robust to the distribution of model descriptors in feature space. We also show that we can address recognition under arbitrary viewpoint by using our framework to facilitate matching of additional features extracted from affine transformed model images. The evaluation of our algorithms in 3D object recognition is demonstrated on a difficult dataset of 620 images.
Cite
Text
Hsiao et al. "Making Specific Features Less Discriminative to Improve Point-Based 3D Object Recognition." IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2010. doi:10.1109/CVPR.2010.5539981Markdown
[Hsiao et al. "Making Specific Features Less Discriminative to Improve Point-Based 3D Object Recognition." IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2010.](https://mlanthology.org/cvpr/2010/hsiao2010cvpr-making/) doi:10.1109/CVPR.2010.5539981BibTeX
@inproceedings{hsiao2010cvpr-making,
title = {{Making Specific Features Less Discriminative to Improve Point-Based 3D Object Recognition}},
author = {Hsiao, Edward and Collet, Alvaro and Hebert, Martial},
booktitle = {IEEE/CVF Conference on Computer Vision and Pattern Recognition},
year = {2010},
pages = {2653-2660},
doi = {10.1109/CVPR.2010.5539981},
url = {https://mlanthology.org/cvpr/2010/hsiao2010cvpr-making/}
}