Zobrazeno 1 - 10
of 1 234
pro vyhledávání: '"Yan, Xuefeng"'
Haze severely degrades the visual quality of remote sensing images and hampers the performance of automotive navigation, intelligent monitoring, and urban management. The emerging denoising diffusion probabilistic model (DDPM) exhibits the significan
Externí odkaz:
http://arxiv.org/abs/2405.09083
Autor:
Gu, Lipeng, Yan, Xuefeng, Nan, Liangliang, Zhu, Dingkun, Chen, Honghua, Wang, Weiming, Wei, Mingqiang
Current methodologies in point cloud analysis predominantly explore 3D geometries, often achieved through the introduction of intricate learnable geometric extractors in the encoder or by deepening networks with repeated blocks. However, these approa
Externí odkaz:
http://arxiv.org/abs/2312.12743
Visual-based measurement systems are frequently affected by rainy weather due to the degradation caused by rain streaks in captured images, and existing imaging devices struggle to address this issue in real-time. While most efforts leverage deep net
Externí odkaz:
http://arxiv.org/abs/2307.09728
Rainy weather significantly deteriorates the visibility of scene objects, particularly when images are captured through outdoor camera lenses or windshields. Through careful observation of numerous rainy photos, we have found that the images are gene
Externí odkaz:
http://arxiv.org/abs/2303.17766
Self-supervised learning is attracting large attention in point cloud understanding. However, exploring discriminative and transferable features still remains challenging due to their nature of irregularity and sparsity. We propose a geometrically an
Externí odkaz:
http://arxiv.org/abs/2303.13100
Autor:
Gu, Lipeng, Yan, Xuefeng, Cui, Peng, Gong, Lina, Xie, Haoran, Wang, Fu Lee, Qin, Jin, Wei, Mingqiang
There is a trend to fuse multi-modal information for 3D object detection (3OD). However, the challenging problems of low lightweightness, poor flexibility of plug-and-play, and inaccurate alignment of features are still not well-solved, when designin
Externí odkaz:
http://arxiv.org/abs/2211.01664
Image dehazing is fundamental yet not well-solved in computer vision. Most cutting-edge models are trained in synthetic data, leading to the poor performance on real-world hazy scenarios. Besides, they commonly give deterministic dehazed images while
Externí odkaz:
http://arxiv.org/abs/2210.16057
Autor:
Wang, Yongzhen, Yan, Xuefeng, Zhang, Kaiwen, Gong, Lina, Xie, Haoran, Wang, Fu Lee, Wei, Mingqiang
Adverse weather conditions such as haze, rain, and snow often impair the quality of captured images, causing detection networks trained on normal images to generalize poorly in these scenarios. In this paper, we raise an intriguing question - if the
Externí odkaz:
http://arxiv.org/abs/2209.01373
Autor:
Wang, Jie, Wang, Yongzhen, Feng, Yidan, Gong, Lina, Yan, Xuefeng, Xie, Haoran, Wang, Fu Lee, Wei, Mingqiang
Image smoothing is a fundamental low-level vision task that aims to preserve salient structures of an image while removing insignificant details. Deep learning has been explored in image smoothing to deal with the complex entanglement of semantic str
Externí odkaz:
http://arxiv.org/abs/2209.00977
Large imbalance often exists between the foreground points (i.e., objects) and the background points in outdoor LiDAR point clouds. It hinders cutting-edge detectors from focusing on informative areas to produce accurate 3D object detection results.
Externí odkaz:
http://arxiv.org/abs/2208.13414