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Learning 2D to 3D Lifting for Object Detection in 3D for Autonomous Vehicles
IROS 2019 | We address the problem of 3D object detection from 2D monocular images in autonomous driving scenarios. We lift the 2D images to 3D representations using learned neural networks and leverage existing networks working directly on 3D data to perform 3D object detection and localization. We show that, with carefully designed training mechanisms and automatically selected minimally noisy data, such a method is not only feasible but gives higher results than many methods working on actual 3D inputs acquired from physical sensors.
Collaborators: Siddharth Srivastava, Frederic Jurie, Gaurav Sharma
