Autonomous driving, in recent years, has been receiving increasing attention for its potential
to relieve drivers' burdens and improve the safety of driving. In modern autonomous driving …

Autonomous vehicles require constant environmental perception to obtain the distribution of
obstacles to achieve safe driving. Specifically, 3D object detection is a vital functional …

Non-uniformed 3D sparse data, eg, point clouds or voxels in different spatial positions, make
contribution to the task of 3D object detection in different ways. Existing basic components in …

LiDAR has become one of the primary 3D object detection sensors in autonomous driving.
However, LiDAR's diverging point pattern with increasing distance results in a non-uniform …

Autonomous driving is regarded as one of the most promising remedies to shield human
beings from severe crashes. To this end, 3D object detection serves as the core basis of …

Current LiDAR-only 3D detection methods inevitably suffer from the sparsity of point clouds.
Many multi-modal methods are proposed to alleviate this issue, while different …

Advances in LiDAR sensors provide rich 3D data that supports 3D scene understanding.
However, due to occlusion and signal miss, LiDAR point clouds are in practice 2.5 D as they …

Three-dimensional object detection from light detection and ranging (LiDAR) point clouds
has gained great attention in recent years due to its wide applications in smart cities and …

Abstract Domain adaptation is crucial to adapt a learned model to new scenarios, such as
domain shifts or changing data distributions. Current approaches usually require a large …

Most object detection methods for autonomous driving usually assume a onsistent feature
distribution between training and testing data, which is not always the case when weathers …