Automated Reconstruction of Building LoDs from Airborne LiDAR Point Clouds Using an Improved Morphological Scale Space

TLDR

Reconstructing building models at multiple levels of detail directly from airborne LiDAR point clouds is essential for balancing user requirements and cost, yet existing approaches rely on costly, inflexible 3D models and do not exploit scale‑space theory for multi‑scale representation. This study introduces a novel method that reconstructs buildings at multiple LoDs from airborne LiDAR point clouds using an improved morphological scale space. The method first isolates building candidates by separating ground and non‑ground points, then iteratively builds a scale space with improved morphological reconstruction to create topological relationship graphs across scales, from which building points are robustly extracted and each building is reconstructed at successive LoDs. Experiments demonstrate that the approach reliably extracts detailed building features, accurately distinguishes buildings from vegetation and other objects, and automatically generates LoDs from the finest point data.

Abstract

Reconstructing building models at different levels of detail (LoDs) from airborne laser scanning point clouds is urgently needed for wide application as this method can balance between the user’s requirements and economic costs. The previous methods reconstruct building LoDs from the finest 3D building models rather than from point clouds, resulting in heavy costs and inflexible adaptivity. The scale space is a sound theory for multi-scale representation of an object from a coarser level to a finer level. Therefore, this paper proposes a novel method to reconstruct buildings at different LoDs from airborne Light Detection and Ranging (LiDAR) point clouds based on an improved morphological scale space. The proposed method first extracts building candidate regions following the separation of ground and non-ground points. For each building candidate region, the proposed method generates a scale space by iteratively using the improved morphological reconstruction with the increase of scale, and constructs the corresponding topological relationship graphs (TRGs) across scales. Secondly, the proposed method robustly extracts building points by using features based on the TRG. Finally, the proposed method reconstructs each building at different LoDs according to the TRG. The experiments demonstrate that the proposed method robustly extracts the buildings with details (e.g., door eaves and roof furniture) and illustrate good performance in distinguishing buildings from vegetation or other objects, while automatically reconstructing building LoDs from the finest building points.