The 3D point cloud (3DPC) has significantly evolved and benefited from the advance of
deep learning (DL). However, the latter faces various issues, including the lack of data or …

Trajectory prediction is a critical component for autonomous vehicles (AVs) to perform safe
planning and navigation. However, few studies have analyzed the adversarial robustness of …

Adversary and invisibility are two fundamental but conflict characters of adversarial
perturbations. Previous adversarial attacks on 3D point cloud recognition have often been …

Deep neural networks on 3D point cloud data have been widely used in the real world,
especially in safety-critical applications. However, their robustness against corruptions is …

Recently, several attempts have demonstrated that 3D deep learning models are as
vulnerable to adversarial example attacks as 2D models. However, these methods are still …

Recent years have witnessed significant advancements in deep learning-based 3D object
detection, leading to its widespread adoption in numerous applications. As 3D object …

Abstract 3D point clouds serve as a crucial data representation in numerous real-world
applications such as autonomous driving, robotics, and medical imaging. While the …

Backdoor attacks for point clouds have elicited mounting interest with the proliferation of
deep learning. The point cloud classifiers can be vulnerable to malicious actors who seek to …

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 …

Deep learning models have been widely used in commercial acoustic systems in recent
years. However, adversarial audio examples can cause abnormal behaviors for those …