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pro vyhledávání: '"Yang,Weidong"'
The task of point cloud upsampling (PCU) is to generate dense and uniform point clouds from sparse input captured by 3D sensors like LiDAR, holding potential applications in real yet is still a challenging task. Existing deep learning-based methods h
Externí odkaz:
http://arxiv.org/abs/2410.15941
Arctic sea ice performs a vital role in global climate and has paramount impacts on both polar ecosystems and coastal communities. In the last few years, multiple deep learning based pan-Arctic sea ice concentration (SIC) forecasting methods have eme
Externí odkaz:
http://arxiv.org/abs/2410.14732
Autor:
Xu, Jingyi, Tu, Siwei, Yang, Weidong, Li, Shuhao, Liu, Keyi, Luo, Yeqi, Ma, Lipeng, Fei, Ben, Bai, Lei
Variation of Arctic sea ice has significant impacts on polar ecosystems, transporting routes, coastal communities, and global climate. Tracing the change of sea ice at a finer scale is paramount for both operational applications and scientific studie
Externí odkaz:
http://arxiv.org/abs/2410.09111
Autor:
Gong, Junchao, Tu, Siwei, Yang, Weidong, Fei, Ben, Chen, Kun, Zhang, Wenlong, Yang, Xiaokang, Ouyang, Wanli, Bai, Lei
Precipitation nowcasting plays a pivotal role in socioeconomic sectors, especially in severe convective weather warnings. Although notable progress has been achieved by approaches mining the spatiotemporal correlations with deep learning, these metho
Externí odkaz:
http://arxiv.org/abs/2410.05805
Self-supervised learning of point cloud aims to leverage unlabeled 3D data to learn meaningful representations without reliance on manual annotations. However, current approaches face challenges such as limited data diversity and inadequate augmentat
Externí odkaz:
http://arxiv.org/abs/2409.04963
Autor:
Ma, Lipeng, Yang, Weidong, Jiang, Sihang, Fei, Ben, Zhou, Mingjie, Li, Shuhao, Xu, Bo, Xiao, Yanghua
Logs play a critical role in providing essential information for system monitoring and troubleshooting. Recently, with the success of pre-trained language models (PLMs) and large language models (LLMs) in natural language processing (NLP), smaller PL
Externí odkaz:
http://arxiv.org/abs/2409.01909
Diffusion models have been widely utilized for image restoration. However, previous blind image restoration methods still need to assume the type of degradation model while leaving the parameters to be optimized, limiting their real-world application
Externí odkaz:
http://arxiv.org/abs/2408.11287
Autor:
Zhang, Rui, Luo, Tianyue, Yang, Weidong, Fei, Ben, Xu, Jingyi, Zhou, Qingyuan, Liu, Keyi, He, Ying
3D Gaussian Splatting (3D-GS) has made a notable advancement in the field of neural rendering, 3D scene reconstruction, and novel view synthesis. Nevertheless, 3D-GS encounters the main challenge when it comes to accurately representing physical refl
Externí odkaz:
http://arxiv.org/abs/2406.05852
Autor:
Fei, Ben, Li, Yixuan, Yang, Weidong, Gao, Hengjun, Xu, Jingyi, Ma, Lipeng, Yang, Yatian, Zhou, Pinghong
The development of medical imaging techniques has made a significant contribution to clinical decision-making. However, the existence of suboptimal imaging quality, as indicated by irregular illumination or imbalanced intensity, presents significant
Externí odkaz:
http://arxiv.org/abs/2406.10236
State-of-the-art 3D models, which excel in recognition tasks, typically depend on large-scale datasets and well-defined category sets. Recent advances in multi-modal pre-training have demonstrated potential in learning 3D representations by aligning
Externí odkaz:
http://arxiv.org/abs/2404.13619