Zobrazeno 1 - 10
of 15 268
pro vyhledávání: '"WU, Jin"'
Deep learning is widely used to predict complex dynamical systems in many scientific and engineering areas. However, the black-box nature of these deep learning models presents significant challenges for carrying out simultaneous data assimilation (D
Externí odkaz:
http://arxiv.org/abs/2410.20072
We study topologically stable defect structures in systems where the defect line classification in three dimensions and associated algebra of interactions (the fundamental group) are governed by the non-Abelian 8-element group, the quaternions Q_8. T
Externí odkaz:
http://arxiv.org/abs/2410.19293
In this paper, we introduce GS-LIVM, a real-time photo-realistic LiDAR-Inertial-Visual mapping framework with Gaussian Splatting tailored for outdoor scenes. Compared to existing methods based on Neural Radiance Fields (NeRF) and 3D Gaussian Splattin
Externí odkaz:
http://arxiv.org/abs/2410.17084
Autor:
Chen, Zhiqiang, Qi, Yuhua, Feng, Dapeng, Zhuang, Xuebin, Chen, Hongbo, Hu, Xiangcheng, Wu, Jin, Peng, Kelin, Lu, Peng
The ability to estimate pose and generate maps using 3D LiDAR significantly enhances robotic system autonomy. However, existing open-source datasets lack representation of geometrically degenerate environments, limiting the development and benchmarki
Externí odkaz:
http://arxiv.org/abs/2409.04961
The stability of visual odometry (VO) systems is undermined by degraded image quality, especially in environments with significant illumination changes. This study employs a deep reinforcement learning (DRL) framework to train agents for exposure con
Externí odkaz:
http://arxiv.org/abs/2408.17005
In autonomous driving, accurately distinguishing between static and moving objects is crucial for the autonomous driving system. When performing the motion object segmentation (MOS) task, effectively leveraging motion information from objects becomes
Externí odkaz:
http://arxiv.org/abs/2408.13790
Autor:
Wang, Jingquan, Zhang, Harry, Unjhawala, Huzaifa Mustafa, Negrut, Peter, Wang, Shu, Slaton, Khailanii, Serban, Radu, Wu, Jin-Long, Negrut, Dan
We introduce SimBench, a benchmark designed to evaluate the proficiency of student large language models (S-LLMs) in generating digital twins (DTs) that can be used in simulators for virtual testing. Given a collection of S-LLMs, this benchmark enabl
Externí odkaz:
http://arxiv.org/abs/2408.11987
Autor:
Li, Yang, Cai, Wen-Qi, Ren, Ji-Gang, Wang, Chao-Ze, Yang, Meng, Zhang, Liang, Wu, Hui-Ying, Chang, Liang, Wu, Jin-Cai, Jin, Biao, Xue, Hua-Jian, Li, Xue-Jiao, Liu, Hui, Yu, Guang-Wen, Tao, Xue-Ying, Chen, Ting, Liu, Chong-Fei, Luo, Wen-Bin, Zhou, Jie, Yong, Hai-Lin, Li, Yu-Huai, Li, Feng-Zhi, Jiang, Cong, Chen, Hao-Ze, Wu, Chao, Tong, Xin-Hai, Xie, Si-Jiang, Zhou, Fei, Liu, Wei-Yue, Liu, Nai-Le, Li, Li, Xu, Feihu, Cao, Yuan, Yin, Juan, Shu, Rong, Wang, Xiang-Bin, Zhang, Qiang, Wang, Jian-Yu, Liao, Sheng-Kai, Peng, Cheng-Zhi, Pan, Jian-Wei
A quantum network provides an infrastructure connecting quantum devices with revolutionary computing, sensing, and communication capabilities. As the best-known application of a quantum network, quantum key distribution (QKD) shares secure keys guara
Externí odkaz:
http://arxiv.org/abs/2408.10994
Large-scale multi-session LiDAR mapping is essential for a wide range of applications, including surveying, autonomous driving, crowdsourced mapping, and multi-agent navigation. However, existing approaches often struggle with data redundancy, robust
Externí odkaz:
http://arxiv.org/abs/2408.03723
Closure models are widely used in simulating complex multiscale dynamical systems such as turbulence and the earth system, for which direct numerical simulation that resolves all scales is often too expensive. For those systems without a clear scale
Externí odkaz:
http://arxiv.org/abs/2408.02965