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pro vyhledávání: '"Ye, Jianglong"'
Autor:
Qiu, Ri-Zhao, Hu, Yafei, Song, Yuchen, Yang, Ge, Fu, Yang, Ye, Jianglong, Mu, Jiteng, Yang, Ruihan, Atanasov, Nikolay, Scherer, Sebastian, Wang, Xiaolong
An open problem in mobile manipulation is how to represent objects and scenes in a unified manner so that robots can use both for navigation and manipulation. The latter requires capturing intricate geometry while understanding fine-grained semantics
Externí odkaz:
http://arxiv.org/abs/2403.07563
Zero-shot novel view synthesis (NVS) from a single image is an essential problem in 3D object understanding. While recent approaches that leverage pre-trained generative models can synthesize high-quality novel views from in-the-wild inputs, they sti
Externí odkaz:
http://arxiv.org/abs/2310.03020
Autor:
Ze, Yanjie, Yan, Ge, Wu, Yueh-Hua, Macaluso, Annabella, Ge, Yuying, Ye, Jianglong, Hansen, Nicklas, Li, Li Erran, Wang, Xiaolong
It is a long-standing problem in robotics to develop agents capable of executing diverse manipulation tasks from visual observations in unstructured real-world environments. To achieve this goal, the robot needs to have a comprehensive understanding
Externí odkaz:
http://arxiv.org/abs/2308.16891
We introduce MVDream, a diffusion model that is able to generate consistent multi-view images from a given text prompt. Learning from both 2D and 3D data, a multi-view diffusion model can achieve the generalizability of 2D diffusion models and the co
Externí odkaz:
http://arxiv.org/abs/2308.16512
Recent works on generalizable NeRFs have shown promising results on novel view synthesis from single or few images. However, such models have rarely been applied on other downstream tasks beyond synthesis such as semantic understanding and parsing. I
Externí odkaz:
http://arxiv.org/abs/2303.12786
We propose to learn to generate grasping motion for manipulation with a dexterous hand using implicit functions. With continuous time inputs, the model can generate a continuous and smooth grasping plan. We name the proposed model Continuous Grasping
Externí odkaz:
http://arxiv.org/abs/2207.05053
Recent development of neural implicit function has shown tremendous success on high-quality 3D shape reconstruction. However, most works divide the space into inside and outside of the shape, which limits their representing power to single-layer and
Externí odkaz:
http://arxiv.org/abs/2204.07126
Tracking and reconstructing 3D objects from cluttered scenes are the key components for computer vision, robotics and autonomous driving systems. While recent progress in implicit function has shown encouraging results on high-quality 3D shape recons
Externí odkaz:
http://arxiv.org/abs/2111.12728
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Akademický článek
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