Interpretable AI for 3D Structural Recognition: A Lightweight Approach to Point Cloud Segmentation

Understanding complex structures in 3D environments is crucial for various real-world applications, from automated inspection systems to geospatial analysis. This paper introduces a novel lightweight AI-driven approach for segmenting structural elements in large-scale 3D point clouds. The method leverages a mathematically grounded framework based on interpretable operators, ensuring robustness against noise and data imbalance while maintaining computational efficiency. Through extensive evaluation on real-world datasets, the model demonstrates superior precision and resource efficiency compared to conventional deep learning architectures. The proposed approach paves the way for scalable, transparent, and adaptable solutions in automated structure recognition and analysis.