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 …

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

False negatives (FN) in 3D object detection, eg, missing predictions of pedestrians, vehicles,
or other obstacles, can lead to potentially dangerous situations in autonomous driving. While …

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 …

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 …

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 …

When localizing and detecting 3D objects for autonomous driving scenes, obtaining
information from multiple sensors (eg, camera, LIDAR) is capable of mutually offering useful …

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

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 …

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 …