The robustness of 3D perception systems under natural corruptions from environments and
sensors is pivotal for safety-critical applications. Existing large-scale 3D perception datasets …

Deep learning (DL) has become one of the mainstream and effective methods for point
cloud analysis tasks such as detection, segmentation and classification. To reduce …

In the realm of modern autonomous driving, the perception system is indispensable for
accurately assessing the state of the surrounding environment, thereby enabling informed …

Robust point cloud parsing under all-weather conditions is crucial to level-5 autonomy in
autonomous driving. However, how to learn a universal 3D semantic segmentation (3DSS) …

Efficient data utilization is crucial for advancing 3D scene understanding in autonomous
driving, where reliance on heavily human-annotated LiDAR point clouds challenges fully …

Abstract Point-, voxel-, and range-views are three representative forms of point clouds. All of
them have accurate 3D measurements but lack color and texture information. RGB images …

Semantic segmentation of point clouds in autonomous driving datasets requires techniques
that can process large numbers of points efficiently. Sparse 3D convolutions have become …

LiDAR semantic segmentation (LSS) is a critical task in autonomous driving and has
achieved promising progress. However prior LSS methods are conventionally investigated …

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 …

The Metaverse, a virtual reality (VR) space where users can interact with each other and
digital objects, is rapidly becoming a reality. As this new world evolves, Artificial Intelligence …