Advances in Mobile LiDAR Point Clouds

Dear Colleagues,

In recent years, remarkable strides have been made in the field of mobile LiDAR technology, particularly in the acquisition and processing of point cloud data. Mobile LiDAR systems, mounted on an ever-increasing variety of platforms, have become instrumental in revolutionizing the way we capture and analyze spatial information. Despite significant advancements, several challenges persist in the processing of mobile LiDAR point cloud. The vast quantities of three-dimensional point cloud data with higher densities lead to challenge in storage, data transfer, efficient request of information, and visualization. Additionallu, achieving accurate registration and alignment of point clouds for creating a seamless and accurate representation of the environment persists as a research issue. Mobile LiDAR is susceptible to noise and occlusions, outliers which impact the point cloud and the capacity to reliably recognize object and extract meaningful features. Given the large volumes of point cloud data, such recognition and feature extraction require advanced automation to reduce the requisite effort and advance processing in a timely manner. Machine learning and artificial intelligence techniques, including deep learning, are increasingly used for such purposes. However, these methods run into difficulties when little data are available in the targeted application context, such as in natural environment (e.g., forests, rivers) or change detection, for instance. Mobile LiDAR systems are often used in conjunction with other sensors (e.g., cameras, GPS) to provide comprehensive information. Integrating and synchronizing data from multiple sources can be challenging.

This Special Issue aims to advance our understanding of how these challenges involved in the processing of mobile LiDAR point clouds can be addressed and overcome. We welcome original work on the following topics:

• data lake for point cloud
• point cloud accuracy and uncertainty
• denoising
• occlusion and completion
• machine and deep learning
• surface and 3D reconstruction
• change detection
• point cloud simulation
• point cloud visualization

Prof. Dr. Sylvie Daniel
Guest Editor

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• data lake for point cloud
• point cloud accuracy and uncertainty
• denoising
• occlusion and completion
• machine and deep learning
• surface and 3D reconstruction
• change detection
• point cloud simulation
• point cloud visualization