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-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 …

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

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 …

Camera and LiDAR are two complementary sensors for 3D object detection in the
autonomous driving context. Camera provides rich texture and color cues while LiDAR …

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 …

Detecting objects from LiDAR point clouds is of tremendous significance in autonomous
driving. In spite of good progress, accurate and reliable 3D detection is yet to be achieved …

Object detection through either RGB images or the LiDAR point clouds has been extensively
explored in autonomous driving. However, it remains challenging to make these two data …

Camera and LiDAR sensor modalities provide complementary appearance and geometric
information useful for detecting 3D objects for autonomous vehicle applications. However …

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 …