Zobrazeno 1 - 10
of 2 805
pro vyhledávání: '"Wu, Xiaofeng"'
Autor:
Mu, Yuxuan, Zuo, Xinxin, Guo, Chuan, Wang, Yilin, Lu, Juwei, Wu, Xiaofeng, Xu, Songcen, Dai, Peng, Yan, Youliang, Cheng, Li
We present GSD, a diffusion model approach based on Gaussian Splatting (GS) representation for 3D object reconstruction from a single view. Prior works suffer from inconsistent 3D geometry or mediocre rendering quality due to improper representations
Externí odkaz:
http://arxiv.org/abs/2407.04237
Spiking Neural Networks (SNNs) offer a promising avenue for energy-efficient computing compared with Artificial Neural Networks (ANNs), closely mirroring biological neural processes. However, this potential comes with inherent challenges in directly
Externí odkaz:
http://arxiv.org/abs/2403.18388
The advent of the Transformer architecture has propelled the growth of natural language processing (NLP) models, leading to remarkable achievements in numerous NLP tasks. Yet, the absence of specialized hardware like expansive GPU memory and high-spe
Externí odkaz:
http://arxiv.org/abs/2403.10504
Autor:
Wu, Xiaofeng, Wang, Zhen, Ding, Zhaoqing, Lin, Zeguo, Yang, Mingyu, Gu, Minghui, Meng, Meng, Yang, Fang, Liu, Xiaoran, Guo, Jiandong
Thin films of the pyrochlore iridates along the [111] direction have drawn significant attention to investigate exotic correlated topological phenomena. Here, we report the fabrication of Eu$_2$Ir$_2$O$_7$ thin films via reactive solid phase epitaxy
Externí odkaz:
http://arxiv.org/abs/2401.04644
Publikováno v:
Proceedings of the Work-in-Progress Papers at the 13th International Conference on Indoor Positioning and Indoor Navigation (IPIN-WiP 2023), September 25 - 28, 2023, Nuremberg, Germany (https://ceur-ws.org/Vol-3581/)
To navigate reliably in indoor environments, an industrial autonomous vehicle must know its position. However, current indoor vehicle positioning technologies either lack accuracy, usability or are too expensive. Thus, we propose a novel concept call
Externí odkaz:
http://arxiv.org/abs/2310.04231
Autor:
Ding, Zhaoqing, Chen, Xuejiao, Wang, Zhenzhen, Zhang, Qinghua, Yang, Fang, Bi, Jiachang, Lin, Ting, Wang, Zhen, Wu, Xiaofeng, Gu, Minghui, Meng, Meng, Cao, Yanwei, Gu, Lin, Zhang, Jiandi, Zhong, Zhicheng, Liu, Xiaoran, Guo, Jiandong
The interplay among symmetry of lattices, electronic correlations, and Berry phase of the Bloch states in solids has led to fascinating quantum phases of matter. A prototypical system is the magnetic Weyl candidate SrRuO3, where designing and creatin
Externí odkaz:
http://arxiv.org/abs/2308.13825
Detecting Resident Space Objects (RSOs) and preventing collisions with other satellites is crucial. Recently, deep convolutional neural networks (DCNNs) have shown superior performance in object detection when large-scale datasets are available. Howe
Externí odkaz:
http://arxiv.org/abs/2305.00412
In the past few years, some alternatives to the Orthogonal Frequency Division Multiplexing (OFDM) modulation have been considered to improve its spectral containment and its performance level in the presence of heavy Doppler shifts. This paper examin
Externí odkaz:
http://arxiv.org/abs/2302.09405
Reconstructing a 3D object from a 2D image is a well-researched vision problem, with many kinds of deep learning techniques having been tried. Most commonly, 3D convolutional approaches are used, though previous work has shown state-of-the-art method
Externí odkaz:
http://arxiv.org/abs/2302.08474
Autor:
Wijayatunga, Minduli, Wu, Xiaofeng
The end of 2019 marked a bushfire crisis for Australia that affected more than 100000km2 of land and destroyed more than 2000 houses. Here, we propose a method of in-orbit bushfire detection with high efficiency to prevent a repetition of this disast
Externí odkaz:
http://arxiv.org/abs/2209.07038