Confidence-Driven Facade Refinement of 3D Building Models Using MLS Point Clouds

TL;DR

This paper introduces an automated framework for refining coarse 3D city models using high-precision MLS data, significantly improving facade accuracy while maintaining topological integrity.

Contribution

It presents a novel method that leverages existing models as priors, integrating surface matching and binary optimization to enhance facade details in urban environments.

Findings

Reduced Cloud-to-Mesh RMSE by approximately 36%

Achieved centimeter-level alignment accuracy

Ensured watertight and manifold geometry

Abstract

Digital twins require continuous maintenance to meet the increasing demand for high-precision geospatial data. However, traditional coarse CityGML building models, typically derived from Airborne Laser Scanning (ALS), often exhibit significant geometric deficiencies, particularly regarding facade accuracy due to the nadir perspective of airborne sensors. Integrating these coarse models with high-precision Mobile Laser Scanning (MLS) data is essential to recover detailed facade geometry. Unlike reconstruction-from-scratch approaches that discard existing semantic information and rely heavily on complete data coverage, this work presents an automated refinement framework that utilizes the coarse model as a geometric prior. This method enables targeted updates to facade geometry even in complex urban environments. It integrates surface matching to identify outdated surfaces and employs a binary integer optimization to select optimal faces from candidate data. Crucially, hard constraints are enforced within the optimization to ensure the topological validity of the refined output. Experimental results demonstrate that the proposed approach effectively corrects facade misalignments, reducing the Cloud-to-Mesh RMSE by approximately 36% and achieving centimeter-level alignment. Furthermore, the framework guarantees strictly watertight and manifold geometry, providing a robust solution for upgrading ALS-derived city models.