RANSAC Algorithm and Distributed Framework for Point Cloud Processing of Ancient Buildings

Abstract

This paper introduces a comprehensive framework for the point cloud processing of traditional buildings. The framework includes segmentation using the RANSAC algorithm and distributed storage based on a fuzzy weighting approach. The methodology employs a height threshold parameter for segmentation to extract building structural elements effectively. Furthermore, an interactive access control model distributes tasks across nodes to achieve load balancing during point cloud matching and analysis. Experimental results demonstrate segmentation accuracy exceeding 99% and alignment time reduction to 967 ms for point cloud models. The distributed computation efficiency reaches 0.8, outperforming conventional methods. The proposed techniques enable accurate dimensional capture, efficient data storage, and information extraction from traditional buildings to support digital preservation.