Autonomous Vehicles aim to understand their surrounding environment by detecting
relevant objects in the scene, which can be performed using a combination of sensors. The …

3D object detection using lidar becomes essential for subsequent vehicle decision-making
and planning as part of an intelligent vehicle perception system. Voxel RCNN is a two-stage …

3D object detection is a fundamental problem in the space of autonomous driving, and
pedestrians are some of the most important objects to detect. The recently introduced …

3D object detection plays a fundamental role in enabling driving automation, which is
regarded as a significant leap forward for contemporary transportation systems from the …

Fueled by substantial advancements in deep learning, the domain of autonomous driving is
swiftly advancing towards more robust and effective intelligent systems. One of the critical …

Accurate object detection in road scenes is one of the most essential requirements of
autonomous vehicles. Based on our findings, the existing solutions for autonomous vehicles …

Detecting 3D objects in a crowd remains a challenging problem since the cars and
pedestrians often gather together and occlude each other in the real world. The Pointpillar is …

Pillar-based 3D object detection methods outperform traditional point-based and voxel-
based methods in terms of speed. However, most of recent methods in this category use …

The development of point cloud-based object detection in the field of autonomous driving
has been rapid. However, it is undeniable that the issue of detecting small objects with high …

LIDAR point cloud and RGB images are often used for object detection in autonomous
driving scenarios. This paper develops a multi-view version of Frustum PointNet (F …