Proposed Method for Detecting Difference Area in Point Cloud Data from Two Time Periods by Generating Hash Values Considering Resolution

Abstract

In recent years, there has been growing interest in using digital twins constructed from point cloud data measured in real-world environments. In Japan, efforts are underway to create digital twins of the national territory using measurement data such as point clouds. However, continuously updating the entire shape of the national territory using all measurement data incurs substantial costs. Therefore, it is necessary to identify techniques that can efficiently update only the areas where changes have occurred, detected from point cloud data of different periods. A recently proposed method detects differences by voxelizing the entire space surrounding the point cloud data and using a Merkle tree. However, this approach cannot detect changes at arbitrary resolutions. To address this problem, our study introduces a method that generates hash values for each hierarchy of the Merkle tree, enabling the detection of changes in point cloud data between two periods at arbitrary resolutions.