The robustness of driving perception systems under unprecedented conditions is crucial for
safety-critical usages. Latest advancements have prompted increasing interests towards …

The past few years have witnessed an increasing interest in improving the perception
performance of LiDARs on autonomous vehicles. While most of the existing works focus on …

Lidar-based object detectors are critical parts of the 3D perception pipeline in autonomous
navigation systems such as self-driving cars. However, they are known to be sensitive to …

LiDAR is crucial for robust 3D scene perception in autonomous driving. LiDAR perception
has the largest body of literature after camera perception. However, multi-task learning …

LiDAR datasets for autonomous driving exhibit biases in properties such as point cloud
density, range, and object dimensions. As a result, object detection networks trained and …

Robust 3D object detection is a core challenge for autonomous mobile systems in field
robotics. To tackle this issue, many researchers have demonstrated improvements in 3D …

LiDAR sensors are used in autonomous driving applications to accurately perceive the
environment. However, they are affected by adverse weather conditions such as snow, fog …

Abstract 3D object detection is a central task for applications such as autonomous driving, in
which the system needs to localize and classify surrounding traffic agents, even in the …

Abstract 3D Object Detectors (3D-OD) are crucial for understanding the environment in
many robotic tasks especially autonomous driving. Including 3D information via Lidar …

LiDAR sensors are becoming crucial for achieving higher levels of autonomy. With the
current sensor technology, LiDAR sensors are still susceptible to erroneous measurements …