Zobrazeno 1 - 10
of 126
pro vyhledávání: '"Chen Yuedong"'
Autor:
Chen, Yuedong, Xu, Haofei, Zheng, Chuanxia, Zhuang, Bohan, Pollefeys, Marc, Geiger, Andreas, Cham, Tat-Jen, Cai, Jianfei
We introduce MVSplat, an efficient model that, given sparse multi-view images as input, predicts clean feed-forward 3D Gaussians. To accurately localize the Gaussian centers, we build a cost volume representation via plane sweeping, where the cross-v
Externí odkaz:
http://arxiv.org/abs/2403.14627
Autor:
Xu, Haofei, Chen, Anpei, Chen, Yuedong, Sakaridis, Christos, Zhang, Yulun, Pollefeys, Marc, Geiger, Andreas, Yu, Fisher
We present Multi-Baseline Radiance Fields (MuRF), a general feed-forward approach to solving sparse view synthesis under multiple different baseline settings (small and large baselines, and different number of input views). To render a target novel v
Externí odkaz:
http://arxiv.org/abs/2312.04565
We present a new generalizable NeRF method that is able to directly generalize to new unseen scenarios and perform novel view synthesis with as few as two source views. The key to our approach lies in the explicitly modeled correspondence matching in
Externí odkaz:
http://arxiv.org/abs/2304.12294
Autor:
Wu, Qianyi, Liu, Xian, Chen, Yuedong, Li, Kejie, Zheng, Chuanxia, Cai, Jianfei, Zheng, Jianmin
The neural implicit representation has shown its effectiveness in novel view synthesis and high-quality 3D reconstruction from multi-view images. However, most approaches focus on holistic scene representation yet ignore individual objects inside it,
Externí odkaz:
http://arxiv.org/abs/2207.09686
Image translation and manipulation have gain increasing attention along with the rapid development of deep generative models. Although existing approaches have brought impressive results, they mainly operated in 2D space. In light of recent advances
Externí odkaz:
http://arxiv.org/abs/2203.10821
Publikováno v:
In Process Safety and Environmental Protection October 2024 190 Part A:226-232
Motion deblurring has witnessed rapid development in recent years, and most of the recent methods address it by using deep learning techniques, with the help of different kinds of prior knowledge. Concerning that deblurring is essentially expected to
Externí odkaz:
http://arxiv.org/abs/2109.08915
Although much progress has been made in visual emotion recognition, researchers have realized that modern deep networks tend to exploit dataset characteristics to learn spurious statistical associations between the input and the target. Such dataset
Externí odkaz:
http://arxiv.org/abs/2107.12096
Publikováno v:
Chinese Journal of Diabetes Mellitus. Feb2024, Vol. 16 Issue 2, p281-284. 4p.
Automatic facial action unit (AU) recognition has attracted great attention but still remains a challenging task, as subtle changes of local facial muscles are difficult to thoroughly capture. Most existing AU recognition approaches leverage geometry
Externí odkaz:
http://arxiv.org/abs/2003.03055