Augmented Reality based Simulated Data (ARSim) with multi-view consistency for AV perception networks

arXiv:2403.15370v1 Announce Type: cross
Abstract: Detecting a diverse range of objects under various driving scenarios is essential for the effectiveness of autonomous driving systems. However, the real-world data collected often lacks the necessary diversity presenting a long-tail distribution. Although synthetic data has been utilized to overcome this issue by generating virtual scenes, it faces hurdles such as a significant domain gap and the substantial efforts required from 3D artists to create realistic environments. To overcome these challenges, we present ARSim, …

abstract arxiv augmented reality autonomous autonomous driving autonomous driving systems cs.cv cs.lg cs.ro data distribution diverse diversity driving however networks objects perception presenting reality simulated data synthetic synthetic data systems type view world