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

In the past decade, deep neural networks have achieved significant progress in point cloud
learning. However, collecting large-scale precisely-annotated point clouds is extremely …

With the maturity of depth sensors, the vulnerability of 3D point cloud models has received
increasing attention in various applications such as autonomous driving and robot …

Multi-modal 3D object detectors are dedicated to exploring secure and reliable perception
systems for autonomous driving (AD). However, while achieving state-of-the-art (SOTA) …

Deploying 3D detectors in unfamiliar domains has been demonstrated to result in a
significant 70-90% drop in detection rate due to variations in lidar, geography, or weather …

Recent self-training techniques have shown notable improvements in unsupervised domain
adaptation for 3D object detection (3D UDA). These techniques typically select pseudo …

3D detection task plays a crucial role in the perception system of intelligent vehicles. LiDAR-
based 3D detectors perform well on particular autonomous driving benchmarks, but may …

Deep learning models for point clouds have shown to be vulnerable to adversarial attacks,
which have received increasing attention in various safety-critical applications such as …

Recent LiDAR-based 3D Object Detection (3DOD) methods show promising results, but they
often do not generalize well to target domains outside the source (or training) data …

Object detection using LiDAR point clouds relies on a large amount of human-annotated
samples when training the underlying detectors' deep neural networks. However, generating …