Xiaoping Wang - IEEE Xplore Author Profile
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Xiaoping Wang

Also published under: X. Wang, Xiao-Ping Wang

Publications
85
Citations
1,508
Publications by Year
20122025
Co-Authors:
Show All Co-Authors (118)
Image of author Xiaoping Wang

Xiaoping Wang

Also published under: X. Wang, Xiao-Ping Wang

Affiliation

School of Artificial Intelligence and Automation and the Key Laboratory of Image Processing and Intelligent Control
Education Ministry of China
Huazhong University of Science and Technology, Wuhan, China

Publication Topics

Biography

Xiaoping Wang was born in Xiangfan, Hubei, China, in 1978. He received the M.S. degree from the Wuhan University of Technology. He is currently pursuing the Ph.D. degree in engineering with the Huazhong University of Science and Technology.,From December 2000 to November 2002, he worked with Wuhan Telecom Device Company, as a Project Manager. From November 2002 to July 2003, he worked with Wuhan Kedi Optical Communication Company Ltd., as a Project Manager. He was a Project Manager, a System Manager, and the Dean of the Research Institute with Shanghai FineMEMS Company Ltd., from August 2003 to June 2009. From July 2009 to December 2017, he worked with Shanghai FineMEMS Company Ltd., as a Deputy General Manager. Since January 2018, he has b...
Publications
85
Citations
1,508
Publications by Year
20122025
Co-Authors:
Show All Co-Authors (118)
Author's Published Works

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Multiscale Semantic Segmentation of Remote Sensing Images Based on Edge Optimization

Wubiao Huang;Fei Deng;Haibing Liu;Mingtao Ding;Qi Yao

IEEE Transactions on Geoscience and Remote Sensing
Year: 2025 | Volume: 63 | Journal Article |
Semantic segmentation of remote sensing images is crucial for disaster monitoring, urban planning, and land use. Due to scene complexity and multiscale features of targets, semantic segmentation of remote sensing images has become a challenging task. Deep convolutional neural networks capture remote contextual dependencies that are limited. Meanwhile, restoring the image size quickly leads to unde...Show More

Multiscale Semantic Segmentation of Remote Sensing Images Based on Edge Optimization

Wubiao Huang;Fei Deng;Haibing Liu;Mingtao Ding;Qi Yao

Year: 2025 | Volume: 63 | Journal Article |

Domain-Incremental Learning for Remote Sensing Semantic Segmentation With Multifeature Constraints in Graph Space

Wubiao Huang;Mingtao Ding;Fei Deng

IEEE Transactions on Geoscience and Remote Sensing
Year: 2024 | Volume: 62 | Journal Article |
Cited by: Papers (1)
The use of deep learning techniques for semantic segmentation in remote sensing has been increasingly prevalent. Effectively modeling remote contextual information and integrating high-level abstract features with low-level spatial features are critical challenges for semantic segmentation tasks. This article addresses these challenges by constructing a graph space reasoning (GSR) module and a dua...Show More

Domain-Incremental Learning for Remote Sensing Semantic Segmentation With Multifeature Constraints in Graph Space

Wubiao Huang;Mingtao Ding;Fei Deng

Year: 2024 | Volume: 62 | Journal Article |

Research on Depth Estimation Method of Single Aerial Image

Wang Gao;Lin Pu;Lei Li;Fei Deng;Lingzhi Bie

2023 IEEE 6th Information Technology,Networking,Electronic and Automation Control Conference (ITNEC)
Year: 2023 | Conference Paper |
Cited by: Papers (1)
Aiming at the problem that the current depth estimation of single image mostly uses the ground public data set, and there is less research on aerial images, this paper uses the collected visible and infrared aerial image data sets to study the depth estimation. We used FCRN and LapDRN to extract the depth estimation results of visible aerial image and infrared aerial image under global and single ...Show More

Research on Depth Estimation Method of Single Aerial Image

Wang Gao;Lin Pu;Lei Li;Fei Deng;Lingzhi Bie

Year: 2023 | Conference Paper |

SphereDepth: Panorama Depth Estimation from Spherical Domain

Qingsong Yan;Qiang Wang;Kaiyong Zhao;Bo Li;Xiaoweo Chu;Fei Deng

2022 International Conference on 3D Vision (3DV)
Year: 2022 | Conference Paper |
Cited by: Papers (1)
The panorama image can simultaneously demonstrate complete information of the surrounding environment and has many advantages in virtual tourism, games, robotics, etc. However, the progress of panorama depth estimation cannot completely solve the problems of distortion and discontinuity caused by the commonly used projection methods. This paper proposes SphereDepth, a novel panorama depth estimati...Show More

SphereDepth: Panorama Depth Estimation from Spherical Domain

Qingsong Yan;Qiang Wang;Kaiyong Zhao;Bo Li;Xiaoweo Chu;Fei Deng

Year: 2022 | Conference Paper |

Efficient Seamline Network Generation for Large-Scale Orthoimage Mosaicking

Junxing Yang;Lulu Liu;Qingsong Yan;Fei Deng

IEEE Geoscience and Remote Sensing Letters
Year: 2022 | Volume: 19 | Journal Article |
Cited by: Papers (3)
Seamline network generation from multiple aerial images with overlapping regions is a key issue for creating seamless and large-scale digital orthophoto maps (DOMs). In this letter, an efficient algorithm is proposed that can find adequate networks from hundreds of aerial images in several minutes. It can also be ensured that the seamline passes through weakly textured regions as much as possible....Show More

Efficient Seamline Network Generation for Large-Scale Orthoimage Mosaicking

Junxing Yang;Lulu Liu;Qingsong Yan;Fei Deng

Year: 2022 | Volume: 19 | Journal Article |

IRS: A Large Naturalistic Indoor Robotics Stereo Dataset to Train Deep Models for Disparity and Surface Normal Estimation

Qiang Wang;Shizhen Zheng;Qingsong Yan;Fei Deng;Kaiyong Zhao;Xiaowen Chu

2021 IEEE International Conference on Multimedia and Expo (ICME)
Year: 2021 | Conference Paper |
Cited by: Papers (15)
Indoor robotics applications heavily rely on scene understanding and reconstruction. Compared to monocular vision, stereo vision methods are more promising to produce accurate geometrical information, such as surface normal and depth/disparity. Besides, deep learning models have shown their superior performance in stereo vision tasks. However, existing stereo datasets rarely contain high-quality s...Show More

IRS: A Large Naturalistic Indoor Robotics Stereo Dataset to Train Deep Models for Disparity and Surface Normal Estimation

Qiang Wang;Shizhen Zheng;Qingsong Yan;Fei Deng;Kaiyong Zhao;Xiaowen Chu

Year: 2021 | Conference Paper |

Panoramic Image Generation Using Centerline- Constrained Mesh Parameterization for Arbitrarily Shaped Tunnel Lining

Fei Deng;Junxing Yang

IEEE Access
Year: 2020 | Volume: 8 | Journal Article |
Cited by: Papers (6)
Three-dimensional (3D) triangular meshes has been widely used in tunnel engineering. This paper proposes an algorithm for unwrapping 3D tunnel lining meshes into straight two-dimensional (2D) meshes to generate a 2D seamless panoramic image of the tunnel lining. The proposed algorithm is divided into three steps. In the first step, the centerline is extracted and the L1-median is used to represent...Show More

Panoramic Image Generation Using Centerline- Constrained Mesh Parameterization for Arbitrarily Shaped Tunnel Lining

Fei Deng;Junxing Yang

Year: 2020 | Volume: 8 | Journal Article |

Textured Building Model Bottom Boundary Rectification Based On DEM

Tianshuo Li;Fei Deng

IGARSS 2019 - 2019 IEEE International Geoscience and Remote Sensing Symposium
Year: 2019 | Conference Paper |
This paper presents a framework for building bottom boundary rectification based on DEM data for removing the gap between the building model reconstructed by interactive modeling and the terrain surface data. First, the regular grid DEM data is converted into mesh data. Then, the positional relationship between building triangle faces and DEM mesh data is calculated through Z-coordinate and inters...Show More

Textured Building Model Bottom Boundary Rectification Based On DEM

Tianshuo Li;Fei Deng

Year: 2019 | Conference Paper |

Steel Structure Building Feature Line Reconstruction from Terrestrial Laser Scanning Point Cloud

Xuefei Li;Ningbo Liang;Xiaoxin Ma;Fei Deng

2018 2nd IEEE Advanced Information Management,Communicates,Electronic and Automation Control Conference (IMCEC)
Year: 2018 | Conference Paper |
T27 dimensional laser scanning technology, which has been developed in recent years, has highlighted the great advantage in the application of reverse engineering. Three dimensional modeling of the characteristic line of steel structure building is a common reverse engineering. This method combined with point cloud data obtained by 3D laser scanning technology to achieve the three-dimensional mode...Show More

Steel Structure Building Feature Line Reconstruction from Terrestrial Laser Scanning Point Cloud

Xuefei Li;Ningbo Liang;Xiaoxin Ma;Fei Deng

Year: 2018 | Conference Paper |

The Detecting Method of Building Deformation Based on Terrestrial Laser Point Cloud

Xu Zhao;Xuefei Li;Hua Liang;Fei Deng;Long Zhou;Weizhen Chen

2016 12th International Conference on Computational Intelligence and Security (CIS)
Year: 2016 | Conference Paper |
Conventional surveying method could not solve the problem of detecting building's whole deformation in a short time, including in particular flatness and inclination values of the surface of the building. Aiming at this problem, this paper introduce a method to solve it based on terrestrial laser point cloud, first step is fitting point-cloud datum to point-cloud model for reducing random errors i...Show More

The Detecting Method of Building Deformation Based on Terrestrial Laser Point Cloud

Xu Zhao;Xuefei Li;Hua Liang;Fei Deng;Long Zhou;Weizhen Chen

Year: 2016 | Conference Paper |

Monitoring the Deformation of the Facade of a Building Based on Terrestrial Laser Point-Cloud

Xu Zhao;Fei Deng;Hua Liang;Long Zhou

2015 11th International Conference on Computational Intelligence and Security (CIS)
Year: 2015 | Conference Paper |
When terrestrial laser point-cloud data are employed for monitoring the façade of a building, point-cloud data collected in different phases cannot be used directly to calculate the deforming displacement due to data points in a homogeneous region caused by inhomogeneous sampling. Aiming at this problem, a triangular patch is built for the previous point-cloud data, the distance is measured betwee...Show More

Monitoring the Deformation of the Facade of a Building Based on Terrestrial Laser Point-Cloud

Xu Zhao;Fei Deng;Hua Liang;Long Zhou

Year: 2015 | Conference Paper |

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