Virtual worlds for 3D Object Detection

Abstract

Training accurate 3D perception algorithms is a challenging task due to the vast amount of annotated data needed. Virtual worlds are gaining popularity for these purposes as they allow to extensively generate realistic 3D ground-truth as well as simulate vehicle-environment interactions. In this talk we will present out research related to the use of synthesized LiDAR data (geometry and semantics) for 3D object detection, monocular depth estimation, and end-to-end driving.