Deep Neural Networks (DNNs) trained on one dataset (source domain) do not perform well
on another set of data (target domain), which is different but has similar properties as the …

The autonomous driving community has witnessed a rapid growth in approaches that
embrace an end-to-end algorithm framework, utilizing raw sensor input to generate vehicle …

Deep learning-based intelligent vehicle perception has been developing prominently in
recent years to provide a reliable source for motion planning and decision making in …

Densely annotating LiDAR point clouds is costly, which often restrains the scalability of fully-
supervised learning methods. In this work, we study the underexplored semi-supervised …

Recently, developing automated video surveillance systems (VSSs) has become crucial to
ensure the security and safety of the population, especially during events involving large …

Recent advancements in vision foundation models (VFMs) have opened up new
possibilities for versatile and efficient visual perception. In this work, we introduce Seal, a …

Transferring knowledge learned from the labeled source domain to the raw target domain for
unsupervised domain adaptation (UDA) is essential to the scalable deployment of …

Active learning, a label-efficient paradigm, empowers models to interactively query an oracle
for labeling new data. In the realm of LiDAR semantic segmentation, the challenges stem …

A unified and versatile LiDAR segmentation model with strong robustness and
generalizability is desirable for safe autonomous driving perception. This work presents …

We introduce LiDAR-UDA, a novel two-stage self-training-based Unsupervised Domain
Adaptation (UDA) method for LiDAR segmentation. Existing self-training methods use a …