An Axial-Oriented Dual-Layer Indexing Structure for Tunnel Point Clouds

Three-dimensional laser scanning technology has increasingly gained favor among professionals in tunnel monitoring. A fundamental challenge in tunnel point cloud processing is to efficiently manage massive datasets using appropriate data structures and accurately extract features such as tunnel axes and cross-sections. However, existing studies often disconnect tunnel point cloud indexing from post-processing tasks. Conventional structures (e.g., voxels, octrees) struggle with long strip-like uneven spatial distribution, resulting in imbalanced trees with numerous empty nodes, which are incompatible with axis-aligned operations. Therefore, this study proposes a dual-layer indexing structure tailored to tunnel geometries. The upper layer reorganizes the tunnel point cloud along its axis, while the lower layer leverages local octrees for fast data querying and updates. In implementation, we introduce a merge-based octree generation strategy for ultra-large-scale datasets, and a rapid Hough transform-based algorithm for tunnel boundaries and axes extraction. Experimental results demonstrate that the proposed method successfully supports the management and visualization of a tunnel point cloud exceeding 6 billion points, significantly enhancing efficiency in narrow tunnel scenarios and streamlining various axis-aligned post-processing tasks.