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 …

In this paper, we present a self-training method, named ST3D++, with a holistic pseudo label
denoising pipeline for unsupervised domain adaptation on 3D object detection. ST3D++ …

Most existing point cloud detection models require large-scale, densely annotated datasets.
They typically underperform in domain adaptation settings, due to geometry shifts caused by …

Sampling discrepancies between different manufacturers and models of lidar sensors result
in inconsistent representations of objects. This leads to performance degradation when 3D …

For a self-driving car to operate reliably, its perceptual system must generalize to the end-
user's environment---ideally without additional annotation efforts. One potential solution is to …

Recently, unsupervised domain adaptation (UDA) for 3D object detectors has increasingly
garnered attention as a method to eliminate the prohibitive costs associated with generating …

In this paper, we present a self-training method, named ST3D++, with a holistic pseudo label
denoising pipeline for unsupervised domain adaptation on 3D object detection. ST3D++ …

LiDAR 3D object detection models are inevitably biased towards their training dataset. The
detector clearly exhibits this bias when employed on a target dataset, particularly towards …

The evolution of autonomous vehicles is advancing rapidly, promising a radical shift in our
future mobility. The cornerstone of building a reliable autonomous vehicle hinges on …