Binary Patterns for Shape Description in RGB-D Object Registration
Abstract
One of the most important tasks in computer vision systems is the description of the local neighborhood around interest points. In RGB-D images, this task is usually performed by computing descriptors encoded as real-value vectors. The use of binary descriptors, like BRISK, BRIEF or ORB, has proven to be adequate to address this task accurately and efficiently. In this paper, we propose a novel binary pattern that encodes the shape around a given point in a RGB-D image with invariance to rotation and scale. This descriptor is contrasted to well-known state-of-the-art 3D descriptors, namely FPFH, Spin Images, SHOT, PFHRGB and CSHOT in order to test its actual performance on the RGB-D Objects dataset. The experiments performed show that the proposed binary pattern descriptor equals and even outperforms state-of-the-art 3D descriptors with a higher computational efficiency.
Cite
Text
Romero-González et al. "Binary Patterns for Shape Description in RGB-D Object Registration." IEEE/CVF Winter Conference on Applications of Computer Vision, 2016. doi:10.1109/WACV.2016.7477727Markdown
[Romero-González et al. "Binary Patterns for Shape Description in RGB-D Object Registration." IEEE/CVF Winter Conference on Applications of Computer Vision, 2016.](https://mlanthology.org/wacv/2016/romerogonzalez2016wacv-binary/) doi:10.1109/WACV.2016.7477727BibTeX
@inproceedings{romerogonzalez2016wacv-binary,
title = {{Binary Patterns for Shape Description in RGB-D Object Registration}},
author = {Romero-González, Cristina and Martínez-Gómez, Jesus and García-Varea, Ismael and Rodríguez-Ruiz, Luis},
booktitle = {IEEE/CVF Winter Conference on Applications of Computer Vision},
year = {2016},
pages = {1-9},
doi = {10.1109/WACV.2016.7477727},
url = {https://mlanthology.org/wacv/2016/romerogonzalez2016wacv-binary/}
}