Search Results for author: Sarthak Sharma

Found 7 papers, 4 papers with code

NMR: Neural Manifold Representation for Autonomous Driving

no code implementations11 May 2022 Unnikrishnan R. Nair, Sarthak Sharma, Midhun S. Menon, Srikanth Vidapanakal

To overcome this limitation, we propose Neural Manifold Representation (NMR), a representation for the task of autonomous driving that learns to infer semantics and predict way-points on a manifold over a finite horizon, centered on the ego-vehicle.

Autonomous Driving

Deep Implicit Surface Point Prediction Networks

no code implementations ICCV 2021 Rahul Venkatesh, Tejan Karmali, Sarthak Sharma, Aurobrata Ghosh, R. Venkatesh Babu, László A. Jeni, Maneesh Singh

Unsigned distance function (UDF) based approaches have been proposed recently as a promising alternative to represent both open and closed shapes.

DUDE: Deep Unsigned Distance Embeddings for Hi-Fidelity Representation of Complex 3D Surfaces

no code implementations4 Nov 2020 Rahul Venkatesh, Sarthak Sharma, Aurobrata Ghosh, Laszlo Jeni, Maneesh Singh

Several implicit 3D shape representation approaches using deep neural networks have been proposed leading to significant improvements in both quality of representations as well as the impact on downstream applications.

3D Shape Representation

INFER: INtermediate representations for FuturE pRediction

1 code implementation26 Mar 2019 Shashank Srikanth, Junaid Ahmed Ansari, Karnik Ram R, Sarthak Sharma, Krishna Murthy J., Madhava Krishna K

Uncharacteristic of state-of-the-art approaches, our representations and models generalize to completely different datasets, collected across several cities, and also across countries where people drive on opposite sides of the road (left-handed vs right-handed driving).

Activity Prediction Future prediction +2

ECHO: An Adaptive Orchestration Platform for Hybrid Dataflows across Cloud and Edge

1 code implementation4 Jul 2017 Pushkara Ravindra, Aakash Khochare, Siva Prakash Reddy, Sarthak Sharma, Prateeksha Varshney, Yogesh Simmhan

ECHO can schedule the dataflow on different Edge, Fog and Cloud resources, and also perform dynamic task migration between resources.

Distributed, Parallel, and Cluster Computing

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