In the pursuit of advancing autonomous driving, 3D object detection stands as a crucial task.
Traditional Light and Ranging Sensors (LiDARs), predominantly positioned atop vehicles …

Current efficient LiDAR-based detection frameworks are lacking in exploiting object
relations, which naturally present in both spatial and temporal manners. To this end, we …

Learned pointcloud representations do not generalize well with an increase in distance to
the sensor. For example, at a range greater than 60 meters, the sparsity of lidar pointclouds …

3D object detection from LiDAR data for autonomous driving has been making remarkable
strides in recent years. Among the state-of-the-art methodologies, encoding point clouds into …

Infrastructure 3D Object Detection is a pivotal component of Vehicle-Infrastructure
Cooperated Autonomous Driving (VICAD). As turning objects account for a high proportion …

Autonomous driving datasets are often skewed and in particular, lack training data for
objects at farther distances from the ego vehicle. The imbalance of data causes a …

The SOTA methods proposed voxelization or pillarization to regularize unordered point
clouds, improving computing efficiency for LiDAR-based 3D object detection. However, they …

LiDAR-based perception is pivotal for ensuring the safety of autonomous driving. Despite
numerous detection methods being continually optimized for both timeliness and accuracy …

Robust and timely perception of the environment is an essential requirement of all
autonomous and semi-autonomous systems. This necessity has been the main factor …

LiDAR-based 3D object detection for autonomous driving has recently drawn the attention of
both academia and industry since it relies upon a sensor that incorporates appealing …