Zobrazeno 1 - 10
of 126
pro vyhledávání: '"RADWAN, NOHA"'
Autor:
Greff, Klaus, Belletti, Francois, Beyer, Lucas, Doersch, Carl, Du, Yilun, Duckworth, Daniel, Fleet, David J., Gnanapragasam, Dan, Golemo, Florian, Herrmann, Charles, Kipf, Thomas, Kundu, Abhijit, Lagun, Dmitry, Laradji, Issam, Hsueh-Ti, Liu, Meyer, Henning, Miao, Yishu, Nowrouzezahrai, Derek, Oztireli, Cengiz, Pot, Etienne, Radwan, Noha, Rebain, Daniel, Sabour, Sara, Sajjadi, Mehdi S. M., Sela, Matan, Sitzmann, Vincent, Stone, Austin, Sun, Deqing, Vora, Suhani, Wang, Ziyu, Wu, Tianhao, Yi, Kwang Moo, Zhong, Fangcheng, Tagliasacchi, Andrea
Data is the driving force of machine learning, with the amount and quality of training data often being more important for the performance of a system than architecture and training details. But collecting, processing and annotating real data at scal
Externí odkaz:
http://arxiv.org/abs/2203.03570
Autor:
Niemeyer, Michael, Barron, Jonathan T., Mildenhall, Ben, Sajjadi, Mehdi S. M., Geiger, Andreas, Radwan, Noha
Neural Radiance Fields (NeRF) have emerged as a powerful representation for the task of novel view synthesis due to their simplicity and state-of-the-art performance. Though NeRF can produce photorealistic renderings of unseen viewpoints when many in
Externí odkaz:
http://arxiv.org/abs/2112.00724
Autor:
Vora, Suhani, Radwan, Noha, Greff, Klaus, Meyer, Henning, Genova, Kyle, Sajjadi, Mehdi S. M., Pot, Etienne, Tagliasacchi, Andrea, Duckworth, Daniel
We present NeSF, a method for producing 3D semantic fields from posed RGB images alone. In place of classical 3D representations, our method builds on recent work in implicit neural scene representations wherein 3D structure is captured by point-wise
Externí odkaz:
http://arxiv.org/abs/2111.13260
Autor:
Sajjadi, Mehdi S. M., Meyer, Henning, Pot, Etienne, Bergmann, Urs, Greff, Klaus, Radwan, Noha, Vora, Suhani, Lucic, Mario, Duckworth, Daniel, Dosovitskiy, Alexey, Uszkoreit, Jakob, Funkhouser, Thomas, Tagliasacchi, Andrea
Publikováno v:
CVPR 2022
A classical problem in computer vision is to infer a 3D scene representation from few images that can be used to render novel views at interactive rates. Previous work focuses on reconstructing pre-defined 3D representations, e.g. textured meshes, or
Externí odkaz:
http://arxiv.org/abs/2111.13152
Autor:
Martin-Brualla, Ricardo, Radwan, Noha, Sajjadi, Mehdi S. M., Barron, Jonathan T., Dosovitskiy, Alexey, Duckworth, Daniel
We present a learning-based method for synthesizing novel views of complex scenes using only unstructured collections of in-the-wild photographs. We build on Neural Radiance Fields (NeRF), which uses the weights of a multilayer perceptron to model th
Externí odkaz:
http://arxiv.org/abs/2008.02268
Publikováno v:
The International Journal of Robotics Research (IJRR), vol. 39, no. 13, pp. 1567-1598, 2020
For mobile robots navigating on sidewalks, it is essential to be able to safely cross street intersections. Most existing approaches rely on the recognition of the traffic light signal to make an informed crossing decision. Although these approaches
Externí odkaz:
http://arxiv.org/abs/1808.06887
Publikováno v:
IEEE Robotics and Automation Letters (RA-L), 3(4):4407-4414, 2018
Semantic understanding and localization are fundamental enablers of robot autonomy that have for the most part been tackled as disjoint problems. While deep learning has enabled recent breakthroughs across a wide spectrum of scene understanding tasks
Externí odkaz:
http://arxiv.org/abs/1804.08366
Localization is an indispensable component of a robot's autonomy stack that enables it to determine where it is in the environment, essentially making it a precursor for any action execution or planning. Although convolutional neural networks have sh
Externí odkaz:
http://arxiv.org/abs/1803.03642
We consider the problem of developing robots that navigate like pedestrians on sidewalks through city centers for performing various tasks including delivery and surveillance. One particular challenge for such robots is crossing streets without pedes
Externí odkaz:
http://arxiv.org/abs/1709.06039