Biography

I am pursuing my master degree in College of Control Science and Engineering, Zhejiang University, Hangzhou, China. My major research interests is feature detection and description, especially for RGB-D images.

Research and Interests

• Feature Detection and Description

Publications

• Xiangrui Zhao, Yu Liu, Zhengbo Wang, Kanzhi Wu, Gamini Dissanayake, and Yong Liu. TG: Accurate and Efficient RGB-D Feature with Texture and Geometric Information. IEEE-ASME Transactions on Mechatronics, 27(4):1973-1981, 2022.
  [BibTeX] [Abstract] [DOI] [PDF]

  Feature extraction and matching are the basis of many computer vision problems, such as image retrieval, object recognition, and visual odometry. In this article, we present a novel RGB-D feature with texture and geometric information (TG). It consists of a keypoint detector and a feature descriptor, which is accurate, efficient, and robust to scene variance. In the keypoint detection, we build a simplified Gaussian image pyramid to extract the texture feature. Meanwhile, the gradient of the point cloud is superimposed as the geometric feature. In the feature description, the texture information and spatial information are encoded in relative order to build a discriminative descriptor. We also construct a novel RGB-D benchmark dataset for RGB-D detector and descriptor evaluation under single variation. Comprehensive experiments are carried out to prove the superior performance of the proposed feature compared with state-of-the-art algorithms. The experimental results also demonstrate that our TG can achieve better performance especially on accuracy and the computational efficiency, making it more suitable for the real-time applications, e.g., visual odometry.

  @article{zhao2022tga,
  title = {TG: Accurate and Efficient RGB-D Feature with Texture and Geometric Information},
  author = {Xiangrui Zhao and Yu Liu and Zhengbo Wang and Kanzhi Wu and Gamini Dissanayake and Yong Liu},
  year = 2022,
  journal = {IEEE-ASME Transactions on Mechatronics},
  volume = {27},
  number = {4},
  pages = {1973-1981},
  doi = {10.1109/TMECH.2022.3175812},
  abstract = {Feature extraction and matching are the basis of many computer vision problems, such as image retrieval, object recognition, and visual odometry. In this article, we present a novel RGB-D feature with texture and geometric information (TG). It consists of a keypoint detector and a feature descriptor, which is accurate, efficient, and robust to scene variance. In the keypoint detection, we build a simplified Gaussian image pyramid to extract the texture feature. Meanwhile, the gradient of the point cloud is superimposed as the geometric feature. In the feature description, the texture information and spatial information are encoded in relative order to build a discriminative descriptor. We also construct a novel RGB-D benchmark dataset for RGB-D detector and descriptor evaluation under single variation. Comprehensive experiments are carried out to prove the superior performance of the proposed feature compared with state-of-the-art algorithms. The experimental results also demonstrate that our TG can achieve better performance especially on accuracy and the computational efficiency, making it more suitable for the real-time applications, e.g., visual odometry.}
  }