In this paper, we propose a new deep architecture for fusing camera and LiDAR sensors for
3D object detection. Because the camera and LiDAR sensor signals have different …

LIDAR point clouds and RGB-images are both extremely essential for 3D object detection.
So many state-of-the-art 3D detection algorithms dedicate in fusing these two types of data …

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 …

In this paper, we focus on exploring the fusion of images and point clouds for 3D object
detection in view of the complementary nature of the two modalities, ie, images possess …

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

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 …

Fusing data from cameras and LiDAR sensors is an essential technique to achieve robust
3D object detection. One key challenge in camera-LiDAR fusion involves mitigating the …

When compared to single modality approaches, fusion-based object detection methods
often require more complex models to integrate heterogeneous sensor data, and use more …

This paper aims to achieve high-accuracy 3D object detection, in which we propose a novel
Spatial-Channel Attention Network (SCANet), a two-stage detector that takes both LIDAR …