… -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 …

… 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 …

… registration is a critical task in many 3D computer vision studies, aiming to … point cloud with
another. In this paper, we propose PANet-a Point-Attention based multi-scale feature fusion …

… 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, …

… There are a few works that use deep networks to combine point clouds and images. MV3D
[4] proposes an element-wise fusion of representations from different domains, based on the …

… 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” …

… 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…

… Besides manually made partial point clouds, we also test our network on the point clouds …
Therefore, we extract partial point clouds from KITTI [43] and NYU Depth V2 [44] datasets, …

… , which could not make full use of the advantages of machine vision for obstacle … , which
are called fusion point cloud data in this study. Second, the fusion point data were filtered and …