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Shivam Duggal

Shivam Duggal is an AI Resident at Uber ATG Toronto supervised by Prof. Raquel Urtasun. Before joining ATG, he received his bachelors degree in computer science from Delhi Technological University in 2017 and then worked as an engineer in Amazon focusing on Machine Learning, alongside doing independent research. His research interests are Computer Vision and Deep Learning.

Research Papers

DeepPruner: Learning Efficient Stereo Matching via Differentiable PatchMatch

S. Duggal, S. Wang, W.-C. Ma, R. Hu, R. Urtasun
We propose a real-time dense depth estimation approach using stereo image pairs, which utilizes differentiable Patch Match to progressively prune the stereo matching search space. Our model achieves competitive performance on the KITTI benchmark despite running in real time. [PDF]
International Conference on Computer Vision (ICCV), 2019