… -based data fusion approaches that leverage both image and point cloud. This review gives
… and point cloud data processing. Followed by in-depth reviews of camera-LiDAR fusion …

… vision, this work proposes a real-time vehicle detection algorithm which fuses vision and lidar
point cloud … by the grid projection method using the lidar point cloud information. Then, the …

… matrix needed to align the source point cloud with target point cloud. This resultant point
cloud, source point cloud aligned, will be the target point cloud in the next mapping iteration. …

… for accurate point-wise segmentation of large-scale point clouds, we develop a deep fusion
… “mini-PointNet” structure for point cloud representation and a novel fusion module (Fig. 2) for …

… reconstructed point cloud as G and the LiDAR reconstructed point cloud as L. The point cloud
patches in G are defined as PG = 1pg1 ,pg2 ,pg3 ··· pgu l. In the same manner, point cloud …

… task of point cloud segmentation to show the effectiveness and benefits of our fusion method.
… and applying an ImageNet CNN for early fusion, eg [11] or intermediate fusion, eg [13], ham…

… We denote X as an arbitrary representation of a point cloud, and any form of point cloud
representation X can be seen as a mapping of the original points P. To note that,“projection” …

… The object detection algorithms can be categorized into 2-D image object detection, 3-D
point cloud object detection, and object detection based on laser and vision fusion. Image …

… vision and stereo, as well as improve the environment perception robustness. Furthermore,
in recent years, the DL has performed well in computer vision … via image and point cloud data …

… object detection (based on point cloud) and 2D object detection (… with 3D point cloud and 2D
image, we project each point to … Regarding the 3D object detection with point cloud as input, …