… point clouds with deep neural networks. Recently, deep learning on point clouds has become
… review of recent progress in deep learning methods for point clouds. It covers three major …

… However, the application of deep learning on 3D point clouds is not easy due to the … nature
of the point clouds. In this paper, the challenges of using point clouds for deep learning are …

… point cloud. In this paper, the recent existing point cloud feature learning methods are classified
as point-… The former directly takes the raw point cloud as the input for deep learning. The …

… provide a comprehensive deep learningbased point cloud semantic … point cloud semantic
segmentation based on deep learning, we introduce several primary datasets for point cloud (…

… We propose a new deep auto-encoder processing unorder point clouds data with lower …
We design a new deep learning-based method for sparse point clouds geometry compression. It …

… For point cloud sequences, points that are close spatially and temporally should be … In
this section, we first briefly review point cloud deep learning techniques. Then we describe the …

… deep learning has achieved remarkable success on image-based tasks, there are many
unique challenges faced by deep … progress of deep learning for 3D point cloud understanding, …

… This article first provides a brief overview of deep learning methods on image and point cloud
data in Section II. In Sections III to VIII, reviews on camera-LiDAR based depth completion, …

… There are many popular expressions of 3D data, such as multi-view, volumetric grid,
mesh and point cloud, which leads to many different deep learning methods on 3D data. The …

… Point cloud registration aims to find a rigid transformation for aligning one point cloud to
another. … The success of deep learning in high-level vision tasks has recently been extended to …