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20192022
most citedDeep Interactive Motion Prediction and Planning: Playing Games with Motion Prediction Models

29 citations · 40 across the 10 of their papers we have counts for

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cs.CV2022

Piecewise Planar Hulls for Semi-Supervised Learning of 3D Shape and Pose from 2D Images

Yigit Baran Can, Alexander Liniger, Danda Pani Paudel +1

We study the problem of estimating 3D shape and pose of an object in terms of keypoints, from a single 2D image. The shape and pose are learned directly from images collected by ca…

cs.CV20224 cited

Uncertainty Guided Policy for Active Robotic 3D Reconstruction using Neural Radiance Fields

Soomin Lee, Le Chen, Jiahao Wang +3

In this paper, we tackle the problem of active robotic 3D reconstruction of an object. In particular, we study how a mobile robot with an arm-held camera can select a favorable num…

cs.CV2022

P3Depth: Monocular Depth Estimation with a Piecewise Planarity Prior

Vaishakh Patil, Christos Sakaridis, Alexander Liniger +1

Monocular depth estimation is vital for scene understanding and downstream tasks. We focus on the supervised setup, in which ground-truth depth is available only at training time.…

cs.CV20224 cited

Adiabatic Quantum Computing for Multi Object Tracking

Jan-Nico Zaech, Alexander Liniger, Martin Danelljan +2

Multi-Object Tracking (MOT) is most often approached in the tracking-by-detection paradigm, where object detections are associated through time. The association step naturally lead…

cs.CV2021

Structured Bird's-Eye-View Traffic Scene Understanding from Onboard Images

Yigit Baran Can, Alexander Liniger, Danda Pani Paudel +1

Autonomous navigation requires structured representation of the road network and instance-wise identification of the other traffic agents. Since the traffic scene is defined on the…

cs.CV2021

End-to-End Urban Driving by Imitating a Reinforcement Learning Coach

Zhejun Zhang, Alexander Liniger, Dengxin Dai +2

End-to-end approaches to autonomous driving commonly rely on expert demonstrations. Although humans are good drivers, they are not good coaches for end-to-end algorithms that deman…