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pro vyhledávání: '"Nigam, Meher Shashwat"'
Autor:
Koprucu, Nursena, Nigam, Meher Shashwat, Xu, Shicheng, Abere, Biruk, Dominici, Gabriele, Rodriguez, Andrew, Vadgama, Sharvaree, Inal, Berfin, Tono, Alberto
Inspired by Geoffrey Hinton emphasis on generative modeling, To recognize shapes, first learn to generate them, we explore the use of 3D diffusion models for object classification. Leveraging the density estimates from these models, our approach, the
Externí odkaz:
http://arxiv.org/abs/2408.06693
Multi-Agent Reinforcement Learning (MARL) has enjoyed significant recent progress thanks, in part, to the integration of deep learning techniques for modeling interactions in complex environments. This is naturally starting to benefit multi-robot sys
Externí odkaz:
http://arxiv.org/abs/2307.03891
Autor:
Pathre, Pranjali, Sahu, Anurag, Rao, Ashwin, Prabhu, Avinash, Nigam, Meher Shashwat, Karandikar, Tanvi, Pandya, Harit, Krishna, K. Madhava
Publikováno v:
IEEE International Conference on Robotics and Biomimetics (ROBIO) 2022
In this paper, we propose and showcase, for the first time, monocular multi-view layout estimation for warehouse racks and shelves. Unlike typical layout estimation methods, MVRackLay estimates multi-layered layouts, wherein each layer corresponds to
Externí odkaz:
http://arxiv.org/abs/2211.16882
The pandemic required efficient allocation of public resources and transforming existing ways of societal functions. To manage any crisis, governments and public health researchers exploit the information available to them in order to make informed d
Externí odkaz:
http://arxiv.org/abs/2211.16360
Autor:
Fedorova, Stanislava, Tono, Alberto, Nigam, Meher Shashwat, Zhang, Jiayao, Ahmadnia, Amirhossein, Bolognesi, Cecilia, Michels, Dominik L.
With the growing interest in deep learning algorithms and computational design in the architectural field, the need for large, accessible and diverse architectural datasets increases. We decided to tackle this problem by constructing a field-specific
Externí odkaz:
http://arxiv.org/abs/2104.12564
Autor:
Nigam, Meher Shashwat, Prabhu, Avinash, Sahu, Anurag, Gupta, Puru, Karandikar, Tanvi, Shankar, N. Sai, Sarvadevabhatla, Ravi Kiran, Krishna, K. Madhava
Given a monocular colour image of a warehouse rack, we aim to predict the bird's-eye view layout for each shelf in the rack, which we term as multi-layer layout prediction. To this end, we present RackLay, a deep neural network for real-time shelf la
Externí odkaz:
http://arxiv.org/abs/2103.09174