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pro vyhledávání: '"Liu Xiuping"'
In this study, we delve into the robustness of neural network-based LiDAR point cloud tracking models under adversarial attacks, a critical aspect often overlooked in favor of performance enhancement. These models, despite incorporating advanced arch
Externí odkaz:
http://arxiv.org/abs/2410.20893
Autor:
Li Fenfang, Liu Xiuping
Publikováno v:
Zeitschrift für Kristallographie - New Crystal Structures, Vol 236, Iss 4, Pp 831-833 (2021)
C36H44Co4N8O26, triclinic, P1‾$‾{1}$ (no. 2), a = 6.6434(4) Å, b = 9.3788(5) Å, c = 18.5581(10) Å, α = 86.262(2)°, β = 89.642(2)°, γ = 84.726(2)°, V = 1148.96(11) Å3, Z = 1, Rgt(F) = 0.0258, ωRref(F2) = 0.0679, T = 298(2) K.
Externí odkaz:
https://doaj.org/article/2839edf8aa734ee68d3316ea581a5b5f
Autor:
Liu Xiuping
Publikováno v:
SHS Web of Conferences, Vol 169, p 01063 (2023)
The effectiveness of ESG performance in promoting corporate innovation has been widely demonstrated. However, a significant research gap remains unexplored that prior scholars have neglected the research into the influence of ESG performance on corpo
Externí odkaz:
https://doaj.org/article/d310eb78430c40fa8e9a15c4d9aad595
In recent years, deep learning-based point cloud normal estimation has made great progress. However, existing methods mainly rely on the PCPNet dataset, leading to overfitting. In addition, the correlation between point clouds with different noise sc
Externí odkaz:
http://arxiv.org/abs/2406.09681
In recent years, point cloud normal estimation, as a classical and foundational algorithm, has garnered extensive attention in the field of 3D geometric processing. Despite the remarkable performance achieved by current Neural Network-based methods,
Externí odkaz:
http://arxiv.org/abs/2406.18541
Learning the skill of human bimanual grasping can extend the capabilities of robotic systems when grasping large or heavy objects. However, it requires a much larger search space for grasp points than single-hand grasping and numerous bimanual graspi
Externí odkaz:
http://arxiv.org/abs/2404.08944
The widespread deployment of Deep Neural Networks (DNNs) for 3D point cloud processing starkly contrasts with their susceptibility to security breaches, notably backdoor attacks. These attacks hijack DNNs during training, embedding triggers in the da
Externí odkaz:
http://arxiv.org/abs/2403.05847
As a cutting-edge biosensor, the event camera holds significant potential in the field of computer vision, particularly regarding privacy preservation. However, compared to traditional cameras, event streams often contain noise and possess extremely
Externí odkaz:
http://arxiv.org/abs/2402.01269
Single Object Tracking in LiDAR point cloud is one of the most essential parts of environmental perception, in which small objects are inevitable in real-world scenarios and will bring a significant barrier to the accurate location. However, the exis
Externí odkaz:
http://arxiv.org/abs/2401.13285
How human interact with objects depends on the functional roles of the target objects, which introduces the problem of affordance-aware hand-object interaction. It requires a large number of human demonstrations for the learning and understanding of
Externí odkaz:
http://arxiv.org/abs/2309.08942