Autonomous driving perception relies on standard sensors such as color cameras and
LiDAR, but each sensor has limitations when sensing complex and diverse environments …

There have been significant advances in neural networks for both 3D object detection using
LiDAR and 2D object detection using video. However, it has been surprisingly difficult to …

LiDAR-Camera fusion-based 3D detection is a critical task for automatic driving. In recent
years, many LiDAR-Camera fusion approaches sprung up and gained promising …

Accurate and reliable perception systems are essential for autonomous driving and robotics.
To achieve this, 3D object detection with multi-sensors is necessary. Existing 3D detectors …

Reliable and accurate 3D object detection is a necessity for safe autonomous driving.
Although LiDAR sensors can provide accurate 3D point cloud estimates of the environment …

Abstract 3D object detection using LiDAR data is an indispensable component for
autonomous driving systems. Yet, only a few LiDAR-based 3D object detection methods …

Abstract 3D object detection is a vital task in many practical applications, such as
autonomous driving, augmented reality and robot navigation. Significant advances have …

Enhancing original LiDAR point cloud features with virtual points has gained widespread
attention in multimodal information fusion. However, existing methods struggle to leverage …

3D object detection is an important task that has been widely applied in autonomous driving.
Recently, fusing multi-modal inputs, ie, LiDAR and camera data, to perform this task has …

Three-dimensional (3D) object detection is essential in autonomous driving. Three-
dimensional (3D) Lidar sensor can capture three-dimensional objects, such as vehicles …