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of 53
pro vyhledávání: '"Xie, Christopher"'
Autor:
Avetisyan, Armen, Xie, Christopher, Howard-Jenkins, Henry, Yang, Tsun-Yi, Aroudj, Samir, Patra, Suvam, Zhang, Fuyang, Frost, Duncan, Holland, Luke, Orme, Campbell, Engel, Jakob, Miller, Edward, Newcombe, Richard, Balntas, Vasileios
We introduce SceneScript, a method that directly produces full scene models as a sequence of structured language commands using an autoregressive, token-based approach. Our proposed scene representation is inspired by recent successes in transformers
Externí odkaz:
http://arxiv.org/abs/2403.13064
Segmenting unseen object instances in cluttered environments is an important capability that robots need when functioning in unstructured environments. While previous methods have exhibited promising results, they still tend to provide incorrect resu
Externí odkaz:
http://arxiv.org/abs/2106.15711
We investigate the use of Neural Radiance Fields (NeRF) to learn high quality 3D object category models from collections of input images. In contrast to previous work, we are able to do this whilst simultaneously separating foreground objects from th
Externí odkaz:
http://arxiv.org/abs/2104.08418
Autor:
Agnew, William, Xie, Christopher, Walsman, Aaron, Murad, Octavian, Wang, Caelen, Domingos, Pedro, Srinivasa, Siddhartha
Learning-based 3D object reconstruction enables single- or few-shot estimation of 3D object models. For robotics, this holds the potential to allow model-based methods to rapidly adapt to novel objects and scenes. Existing 3D reconstruction technique
Externí odkaz:
http://arxiv.org/abs/2009.13146
Segmenting unseen objects in cluttered scenes is an important skill that robots need to acquire in order to perform tasks in new environments. In this work, we propose a new method for unseen object instance segmentation by learning RGB-D feature emb
Externí odkaz:
http://arxiv.org/abs/2007.15157
In order to function in unstructured environments, robots need the ability to recognize unseen objects. We take a step in this direction by tackling the problem of segmenting unseen object instances in tabletop environments. However, the type of larg
Externí odkaz:
http://arxiv.org/abs/2007.08073
Publikováno v:
In iScience 17 November 2023 26(11)
In order to function in unstructured environments, robots need the ability to recognize unseen novel objects. We take a step in this direction by tackling the problem of segmenting unseen object instances in tabletop environments. However, the type o
Externí odkaz:
http://arxiv.org/abs/1907.13236
We consider the problem of providing dense segmentation masks for object discovery in videos. We formulate the object discovery problem as foreground motion clustering, where the goal is to cluster foreground pixels in videos into different objects.
Externí odkaz:
http://arxiv.org/abs/1812.02772
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