Zobrazeno 1 - 10
of 50 515
pro vyhledávání: '"point-cloud data"'
Autor:
Ding, Shihai1 (AUTHOR), Chen, Xiaoping1 (AUTHOR) cducxp@cdu.edu.cn, Ai, Changfa2,3 (AUTHOR), Wang, Jingang1 (AUTHOR), Yang, Huaping1 (AUTHOR)
Publikováno v:
Scientific Reports. 7/18/2024, Vol. 14 Issue 1, p1-17. 17p.
Autonomous agents require the capability to identify dynamic objects in their environment for safe planning and navigation. Incomplete and erroneous dynamic detections jeopardize the agent's ability to accomplish its task. Dynamic detection is a chal
Externí odkaz:
http://arxiv.org/abs/2410.18638
Autor:
ZBIROVSKÝ, SLÁVEK1 slavek.zbirovsky@fsv.cvut.cz, NEŽERKA, VÁCLAV1
Publikováno v:
Acta Polytechnica CTU Proceedings. 2024, Vol. 49, p92-96. 5p.
Autor:
Bungula, Wako, Darcy, Isabel
Carlsson, Singh and Memoli's TDA mapper takes a point cloud dataset and outputs a graph that depends on several parameter choices. Dey, Memoli, and Wang developed Multiscale Mapper for abstract topological spaces so that parameter choices can be anal
Externí odkaz:
http://arxiv.org/abs/2409.17360
Textile pilling assessment is critical for textile quality control. We collect thousands of 3D point cloud images in the actual test environment of textiles and organize and label them as TextileNet8 dataset. To the best of our knowledge, it is the f
Externí odkaz:
http://arxiv.org/abs/2408.10496
Publikováno v:
IEEE Robotics and Automation Letters, 2024
This paper introduces an end-to-end trajectory planning algorithm tailored for multi-UAV systems that generates collision-free trajectories in environments populated with both static and dynamic obstacles, leveraging point cloud data. Our approach co
Externí odkaz:
http://arxiv.org/abs/2406.19742
Publikováno v:
In Automation in Construction February 2025 170
Creating geometric digital twins (gDT) for as-built roads still faces many challenges, such as low automation level and accuracy, limited asset types and shapes, and reliance on engineering experience. A novel scan-to-building information modeling (s
Externí odkaz:
http://arxiv.org/abs/2406.12404
Publikováno v:
Transactions of the Chinese Society of Agricultural Engineering. Nov2024, Vol. 40 Issue 22, p290-296. 7p.
In this paper, we introduce a novel method for comparing 3D point clouds, a critical task in various machine learning applications. By interpreting point clouds as samples from underlying probability density functions, the statistical manifold struct
Externí odkaz:
http://arxiv.org/abs/2405.04864