Point Cloud Processing
Submission deadline: 2023-12-30
Section Collection Editors

Section Collection Information

Dear Colleagues,

In our 3D world, point clouds, using a huge collection of individual 3D points together to represent a 3-D shape, object or scenes, provide a means of assembling only points to describe shape, conveying more details and occupying less storage memory than meshes or voxels as they contain no underlying topology and connection information between points. Point clouds that are typically generated using 3D scanners (laser scanners, time-of-flight scanners, structured-light scanners and RGB-D sensors) or reconstructed from images, became a generally used representation for 3D objects and has been widely applied in various fields such as computer aided design, reverse engineering, digital modeling, visualization and animation. Point cloud processing methods including point cloud registration, matching, recognition, segmentation, classification, feature extraction, non-realistic rendering, also supporting mass highly advanced applications, for example, driver assistance systems, robot navigation and perception, and virtual reality (VR). Nowadays, with high-resolution 3D scanning devices and AI technologies showing up, new point cloud processing methods and applications on large scale real-world scenes/objects, like cultural heritage modeling, have evolved significantly. However, a lot of interesting problems still remain open, such as integrating point clouds with other data to form multi-modal datasets, generating and editing point clouds by given texts, using point clouds to produce animation, et al.

Thus, we are interested in new techniques and methods in the framework of point cloud processing, including point cloud fusion, registration, matching, recognition, segmentation, classification, feature extraction, non-realistic rendering, generating and editing, et al.

For this, it is important to collect the work on different subjects which is closely related with point clouds. Research articles and reviews in this area of study are welcome.

We look forward to receiving your contributions.

Dr. Yuhe Zhang

Section Editors


Point Clouds; Registration; Matching; Recognition; Segmentation; Classification; Feature Extraction; Non-Realistic Rendering; Multi-Modal Datasets; Point Clouds Generating and Editing

Published Paper