Zobrazeno 1 - 10
of 653
pro vyhledávání: '"Wang, Zhirui"'
Autor:
Wang, Zhechao, Cheng, Peirui, Tian, Pengju, Wang, Yuchao, Chen, Mingxin, Duan, Shujing, Wang, Zhirui, Li, Xinming, Sun, Xian
Remote sensing lightweight foundation models have achieved notable success in online perception within remote sensing. However, their capabilities are restricted to performing online inference solely based on their own observations and models, thus l
Externí odkaz:
http://arxiv.org/abs/2406.07032
Autor:
Wang, Yuchao, Cheng, Peirui, Tian, Pengju, Yuan, Ziyang, Zhao, Liangjin, Tian, Jing, Wang, Wensheng, Wang, Zhirui, Sun, Xian
With the advancement of collaborative perception, the role of aerial-ground collaborative perception, a crucial component, is becoming increasingly important. The demand for collaborative perception across different perspectives to construct more com
Externí odkaz:
http://arxiv.org/abs/2406.04647
Autor:
Tian, Pengju, Cheng, Peirui, Wang, Yuchao, Wang, Zhechao, Wang, Zhirui, Yan, Menglong, Yang, Xue, Sun, Xian
Multi-UAV collaborative 3D object detection can perceive and comprehend complex environments by integrating complementary information, with applications encompassing traffic monitoring, delivery services and agricultural management. However, the extr
Externí odkaz:
http://arxiv.org/abs/2406.04648
Thanks to the application of deep learning technology in point cloud processing of the remote sensing field, point cloud segmentation has become a research hotspot in recent years, which can be applied to real-world 3D, smart cities, and other fields
Externí odkaz:
http://arxiv.org/abs/2405.19735
Research on multi-view stereo based on remote sensing images has promoted the development of large-scale urban 3D reconstruction. However, remote sensing multi-view image data suffers from the problems of occlusion and uneven brightness between views
Externí odkaz:
http://arxiv.org/abs/2405.17140
Autor:
Wang, Zhechao, Cheng, Peirui, Chen, Mingxin, Tian, Pengju, Wang, Zhirui, Li, Xinming, Yang, Xue, Sun, Xian
Collaborative trajectory prediction can comprehensively forecast the future motion of objects through multi-view complementary information. However, it encounters two main challenges in multi-drone collaboration settings. The expansive aerial observa
Externí odkaz:
http://arxiv.org/abs/2405.14674
Autor:
Wang, Yuelei, Zhang, Ting, Zhao, Liangjin, Hu, Lin, Wang, Zhechao, Niu, Ziqing, Cheng, Peirui, Chen, Kaiqiang, Zeng, Xuan, Wang, Zhirui, Wang, Hongqi, Sun, Xian
In recent years, remote sensing (RS) vision foundation models such as RingMo have emerged and achieved excellent performance in various downstream tasks. However, the high demand for computing resources limits the application of these models on edge
Externí odkaz:
http://arxiv.org/abs/2309.09003
Autor:
Wang, Zhechao, Cheng, Peirui, Duan, Shujing, Chen, Kaiqiang, Wang, Zhirui, Li, Xinming, Sun, Xian
Onboard intelligent processing is widely applied in emergency tasks in the field of remote sensing. However, it is predominantly confined to an individual platform with a limited observation range as well as susceptibility to interference, resulting
Externí odkaz:
http://arxiv.org/abs/2309.02230
Autor:
Mao, Yongqiang, Chen, Kaiqiang, Zhao, Liangjin, Chen, Wei, Tang, Deke, Liu, Wenjie, Wang, Zhirui, Diao, Wenhui, Sun, Xian, Fu, Kun
Building 3D reconstruction from remote sensing images has a wide range of applications in smart cities, photogrammetry and other fields. Methods for automatic 3D urban building modeling typically employ multi-view images as input to algorithms to rec
Externí odkaz:
http://arxiv.org/abs/2301.04581
Machine learning approaches have become popular for molecular modeling tasks, including molecular force fields and properties prediction. Traditional supervised learning methods suffer from scarcity of labeled data for particular tasks, motivating th
Externí odkaz:
http://arxiv.org/abs/2211.14429