Junjie Liu
Current Organizations / Institutes
The organizations/institutes from the scholar's latest article
Research Subjects
Junjie Liu has a total of 7 co-authors. Here are the top co-authors by number of shared publications:
- Huan Jiang
Guangdong University of Technology
2 shared publications - Jiahui Wang
School of Physics, Sun Yat-Sen University, Guangzhou 510275, China
2 shared publications - Keming Li
School of Physics and Optoelectronic Engineering, Guangdong University of Technology, Guangzhou 510006, China
2 shared publications - Weiren Zhao
School of Physics and Optoelectronic Engineering, Guangdong University of Technology, Guangzhou 510006, China
2 shared publications - Hui Zhang
School of Physics and Optoelectronic Engineering, Guangdong University of Technology, Guangzhou, China
1 shared publication - Hui Zhang
School of Physics and Optoelectronic Engineering, Guangdong University of Technology, Guangzhou 510006, China
1 shared publication
Latest Publications
- Open Access
A Segment-Based Algorithm for Grid Junction Corner Detection Used in Stereo Microscope Camera Calibration
Corner detection is responsible for accurate camera calibration, which is an essential task for binocular three-dimensional (3D) reconstruction. In microscopic scenes, binocular 3D reconstruction has significant potential to achieve fast and accurate measurements. However, traditional corner detectors and calibration patterns (checkerboard) performed poorly in microscopic scenes due to the non-uniform illumination and the shallow depth of field of the microscope. In this paper, we present a novel method for detecting grid junction corners based on image segmentation, offering a robust alternative to the traditional checkerboard pattern. Model fitting was utilized to obtain the coordinates at a sub-pixel level. The procedures of the proposed method were elaborated, including image segmentation, corner prediction, and model fitting. The mathematical model was established to describe the grid junction. The experiment was conducted using both synthetic and real data and the experimental result shows that this method achieves high precision and is robust to image blurring, indicating this method is suitable for microscope camera calibration.
Open AccessA Segment-Based Algorithm for Grid Junction Corner Detection Used in Stereo Microscope Camera Calibration
Corner detection is responsible for accurate camera calibration, which is an essential task for binocular three-dimensional (3D) reconstruction. In microscopic scenes, binocular 3D reconstruction has significant potential to achieve fast and accurate measurements. However, traditional corner detectors and calibration patterns (checkerboard) performed poorly in microscopic scenes due to the non-uniform illumination and the shallow depth of field of the microscope. In this paper, we present a novel method for detecting grid junction corners based on image segmentation, offering a robust alternative to the traditional checkerboard pattern. Model fitting was utilized to obtain the coordinates at a sub-pixel level. The procedures of the proposed method were elaborated, including image segmentation, corner prediction, and model fitting. The mathematical model was established to describe the grid junction. The experiment was conducted using both synthetic and real data and the experimental result shows that this method achieves high precision and is robust to image blurring, indicating this method is suitable for microscope camera calibration.
- Open Access
- 21 March 2024
- Published by MDPI AG in Electronics
- Vol. 13 (6) , 1155
FuNet: Multi-Feature Fusion for Point Cloud Completion Network
The densification of a point cloud is a crucial challenge in visual applications, particularly when estimating a complete and dense point cloud from a local and incomplete one. This paper introduces a point cloud completion network named FuNet to address this issue. Current point cloud completion networks adopt various methodologies, including point-based processing and convolution-based processing. Unlike traditional shape completion approaches, FuNet combines point-based processing and convolution-based processing to extract their features, and fuses them through an attention module to generate a complete point cloud from 1024 points to 16,384 points. The experimental results show that when comparing the optimal completion networks, FuNet decreases the CD by 5.17% and increases the F-score by 4.75% on the ShapeNet dataset. In addition, FuNet achieves better results in most categories on a small sample dataset.
- 21 March 2024
- Published by MDPI AG in Electronics
- Vol. 13 (6) , 1155
Open AccessFuNet: Multi-Feature Fusion for Point Cloud Completion Network
The densification of a point cloud is a crucial challenge in visual applications, particularly when estimating a complete and dense point cloud from a local and incomplete one. This paper introduces a point cloud completion network named FuNet to address this issue. Current point cloud completion networks adopt various methodologies, including point-based processing and convolution-based processing. Unlike traditional shape completion approaches, FuNet combines point-based processing and convolution-based processing to extract their features, and fuses them through an attention module to generate a complete point cloud from 1024 points to 16,384 points. The experimental results show that when comparing the optimal completion networks, FuNet decreases the CD by 5.17% and increases the F-score by 4.75% on the ShapeNet dataset. In addition, FuNet achieves better results in most categories on a small sample dataset.