Zobrazeno 1 - 10
of 190
pro vyhledávání: '"Shibiao Xu"'
Autor:
Xiaobo Lin, Shibiao Xu
Publikováno v:
Applied Sciences, Vol 14, Iss 6, p 2425 (2024)
In unmanned aerial vehicle (UAV) large-scale scene modeling, challenges such as missed shots, low overlap, and data gaps due to flight paths and environmental factors, such as variations in lighting, occlusion, and weak textures, often lead to incomp
Externí odkaz:
https://doaj.org/article/c53f2a95cae54c9e9c1bc680e3311f83
Publikováno v:
Tongxin xuebao, Vol 43, Pp 143-152 (2022)
A new challenge for multi-view learning was posed by corrupted view-correspondences.To address this issue， an effective multi-view learning method for view-unaligned data was proposed.First，to capture cross-view latent affinity in multi-view hete
Externí odkaz:
https://doaj.org/article/7045a8bb6f564f40bb5fe9c373c4ad6e
Publikováno v:
Remote Sensing, Vol 15, Iss 12, p 3155 (2023)
High-fidelity mesh reconstruction from point clouds has long been a fundamental research topic in computer vision and computer graphics. Traditional methods require dense triangle meshes to achieve high fidelity, but excessively dense triangles may l
Externí odkaz:
https://doaj.org/article/a46568a994164ad1957649c7595a97f8
Publikováno v:
Remote Sensing, Vol 15, Iss 8, p 1957 (2023)
Existing architecture semantic modeling methods in 3D complex urban scenes continue facing difficulties, such as limited training data, lack of semantic information, and inflexible model processing. Focusing on extracting and adopting accurate semant
Externí odkaz:
https://doaj.org/article/91f127b45c4645e1b5786517ce6b75e2
Publikováno v:
Remote Sensing, Vol 15, Iss 6, p 1625 (2023)
Traditional multi-view stereo (MVS) is not applicable for the point cloud reconstruction of serialized video frames. Among them, the exhausted feature extraction and matching for all the prepared frames are time-consuming, and the scope of the search
Externí odkaz:
https://doaj.org/article/984ef18778c44f5fbae151a5dca31c70
Publikováno v:
IEEE Access, Vol 9, Pp 72451-72464 (2021)
The intelligent grasping expects that the manipulator has the ability to grasp objects with high degree of freedom in a wild (unstructured) environment. Due to low perception ability in handing targets and environments, most industrial robots are lim
Externí odkaz:
https://doaj.org/article/c7340e04f55b491281c927eff74453cf
Publikováno v:
IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, Vol 14, Pp 11500-11507 (2021)
A georeferenced orthophoto built from aerial images is the basic resource for various of remote sensing applications. As traditional low flight altitude aerial photographic survey is hard to handle large areas, higher altitude brings better survey ef
Externí odkaz:
https://doaj.org/article/91a764af05a54cbfa8461bc7a00605ba
Publikováno v:
IET Computer Vision, Vol 14, Iss 7, Pp 482-492 (2020)
Stereo image completion (SIC) is to fill holes existing in a pair of stereo images. SIC is more complicated than single image repairing, which needs to complete the pair of images while keeping their stereoscopic consistency. In recent years, deep le
Externí odkaz:
https://doaj.org/article/f42e0b5f131e4bb7b95844213aa646d3
Publikováno v:
Remote Sensing, Vol 14, Iss 23, p 6022 (2022)
Real-time large-scale point cloud segmentation is an important but challenging task for practical applications such as remote sensing and robotics. Existing real-time methods have achieved acceptable performance by aggregating local information. Howe
Externí odkaz:
https://doaj.org/article/ca5aa1c7c4434c7eb3d387062eeb8a52
Autor:
Pengcheng Han, Cunbao Ma, Jian Chen, Lin Chen, Shuhui Bu, Shibiao Xu, Yong Zhao, Chenhua Zhang, Tatsuya Hagino
Publikováno v:
Remote Sensing, Vol 14, Iss 16, p 4113 (2022)
Individual tree counting (ITC) is a popular topic in the remote sensing application field. The number and planting density of trees are significant for estimating the yield and for futher planing, etc. Although existing studies have already achieved
Externí odkaz:
https://doaj.org/article/62568ef7f144441c9745b470961cafff