Autonomous driving, in recent years, has been receiving increasing attention for its potential
to relieve drivers' burdens and improve the safety of driving. In modern autonomous driving …

The virtual-to-real paradigm, ie, training models on virtual data and then applying them to
solve real-world problems, has attracted more and more attention from various domains by …

Rigorously testing autonomy systems is essential for making safe self-driving vehicles (SDV)
a reality. It requires one to generate safety critical scenarios beyond what can be collected …

This paper aims to tackle the problem of modeling dynamic urban streets for autonomous
driving scenes. Recent methods extend NeRF by incorporating tracked vehicle poses to …

Nowadays, and more than a decade after the first steps towards autonomous driving, we
keep heading to achieve fully autonomous vehicles on our roads, with LiDAR sensors being …

Autonomous driving systems have witnessed significant development during the past years
thanks to the advance in machine learning-enabled sensing and decision-making …

Dominated point cloud-based 3D object detectors in autonomous driving scenarios rely
heavily on the huge amount of accurately labeled samples, however, 3D annotation in the …

End-to-End driving is a promising paradigm as it circumvents the drawbacks associated with
modular systems, such as their overwhelming complexity and propensity for error …

Abstract We present Neural Fields for LiDAR (NFL), a method to optimise a neural field
scene representation from LiDAR measurements, with the goal of synthesizing realistic …

We present LiDARGen, a novel, effective, and controllable generative model that produces
realistic LiDAR point cloud sensory readings. Our method leverages the powerful score …