Tanmay Gupta

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Piotr Dollár7
C. Lawrence Zitnick7
Pushmeet Kohli6
Andrew W. Fitzgibbon6
Shuicheng Yan5
5Hongzhi Wu
5Dongping Li
5Angela Dai
5Charles Ruizhongtai Qi
5Matthias Nießner

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2005
2017
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Completing 3D object shape from one depth image
Our goal is to recover a complete 3D model from a depth image of an object. Existing approaches rely on user interaction or apply to a limited class of objects, such as chairs. We aim to fully automatically reconstruct a 3D model from any category. We take an exemplar-based approach: retrieve similar objects in a database of 3D models using view-based… (More)
An architecture for congestion management in Ethernet clusters
Interconnects for clusters and bladed systems must deliver efficient throughput, low latency, low delay variations and minimal frame drops. The primary technical issues hindering Ethernet adoption for cluster and blade system interconnects are the current methods Ethernet switches use for dealing with congestion, which can happen frequently under cluster… (More)
3DFS: Deformable Dense Depth Fusion and Segmentation for Object Reconstruction from a Handheld Camera
We propose an approach for 3D reconstruction and segmentation of a single object placed on a flat surface from an input video. Our approach is to perform dense depth map estimation for multiple views using a proposed objective function that preserves detail. The resulting depth maps are then fused using a proposed implicit surface function that is robust to… (More)
Decentralized Random Walk-Based Data Collection in Networks
We analyze a decentralized random-walk based algorithm for data collection at the sink in a multi-hop sensor network. Our algorithm, Metropolis-Collect, which involves data packets being passed to random neighbors in the network according to a simple metropolis randomwalk mechanism, requires no configuration and incurs no routing overhead. To analyze this… (More)
Aligned Image-Word Representations Improve Inductive Transfer Across Vision-Language Tasks
A grand goal of computer vision is to build systems that learn visual representations over time that can be applied to many tasks. In this paper, we investigate a vision-language embedding as a core representation and show that it leads to better cross-task transfer than standard multi-task learning. In particular, the task of visual recognition is aligned… (More)
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