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 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 …

LiDAR and camera are two essential sensors for 3D object detection in autonomous driving.
LiDAR provides accurate and reliable 3D geometry information while the camera provides …

Abstract 3D object detection is a challenging and promising task for autonomous driving and
robotics, benefiting significantly from multi-sensor fusion, such as LiDAR and cameras …

Lidars and cameras are critical sensors that provide complementary information for 3D
detection in autonomous driving. While prevalent multi-modal methods simply decorate raw …

LiDAR-based 3D detection is critical in autonomous driving perception systems. However,
point-based 3D object detection that directly learns from point clouds is challenging owing to …

Prior works in voxel-based Lidar 3D object detection have demonstrated promising results in
detecting a variety of road objects such as cars, pedestrians, and cyclists. However, these …

Augmenting LiDAR input with multiple previous frames provides richer semantic information
and thus boosts performance in 3D object detection, However, crowded point clouds in multi …

LiDAR-camera fusion methods have shown impressive performance in 3D object detection.
Recent advanced multi-modal methods mainly perform global fusion, where image features …

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 …