LiDAR and camera are two common sensors to collect data in time for 3D object detection
under the autonomous driving context. Though the complementary information across …

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

3D object detection based on LiDAR-camera fusion is becoming an emerging research
theme for autonomous driving. However, it has been surprisingly difficult to effectively fuse …

In autonomous driving, LiDAR point-clouds and RGB images are two major data modalities
with complementary cues for 3D object detection. However, it is quite difficult to sufficiently …

Fusing LiDAR and camera information is essential for accurate and reliable 3D object
detection in autonomous driving systems. This is challenging due to the difficulty of …

Recently, the progress in autonomous driving tries to leverage the strong complementarity of
LiDAR point clouds and RGB images to realize a high-efficient 3D object detection task …

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 …

Integrating LiDAR and Camera information into Bird's-Eye-View (BEV) has become an
essential topic for 3D object detection in autonomous driving. Existing methods mostly adopt …

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 and camera are two important sensors for 3D object detection in autonomous driving.
Despite the increasing popularity of sensor fusion in this field, the robustness against inferior …