Quality assessment of 3D building data

Abstract

Three‐dimensional building models are often now produced from lidar and photogrammetric data. The quality control of these models is a relevant issue both from the scientific and practical points of view. This work presents a method for the quality control of such models. The input model (3D building data) is co‐registered to the verification data using a 3D surface matching method. The 3D surface matching evaluates the Euclidean distances between the verification and input data‐sets. The Euclidean distances give appropriate metrics for the 3D model quality. This metric is independent of the method of data capture. The proposed method can favourably address the reference system accuracy, positional accuracy and completeness. Three practical examples of the method are provided for demonstration.

Metric Evaluation Pipeline for 3d Modeling of Urban Scenes

“…Source imagery and ground truth data used for experiments have also been publicly released in the Multi-View Stereo 3D Mapping Challenge (MVS3DM) Benchmark (2017). 3D model evaluation metrics in our pipeline include horizontal and vertical accuracy and completeness (similar to metrics employed by Akca et al 2010, Bosch et al 2016, and Sampath et al 2014, volumetric completeness and correctness (similar to work reported by McKeown et al 2000), perceptual quality (based on the work of Lavoue et al 2013), and model simplicity (a relative measure of triangle or geon count). These metrics are intended to expand upon the multiple view stereo analysis by Bosch et al (2016) and enable a comprehensive automated performance evaluation of both geometric and perceptual value of 3D object models reconstructed from imagery as well as assessment of the modeling process at the point cloud reconstruction, semantic labeling, and mesh simplification or model fitting steps.…”

Building reconstruction from airborne laser scanning data

“…The good point of this method is that the quality evaluation method is independent of the data sources and coordinate systems because the co-registration can finally transform the verification and input datasets into the same coordinate system. Similar to (42,43) has difficulties in evaluating the completeness of the model and analysing a needed correction of the roof topology. Until now, it is not easy to give standard methods and quantified parameters that can satisfy the industrial requirements of 3D building model quality evaluation.…”

“…Only analysing the distances between data and models at point level is not enough to give a comprehensive evaluation of the modelling quality. Akca et al (43) proposed a method to coregister the input model to the verification data by means of the Least Squares 3D surface matching method and evaluate the Euclidean distances between the verification and input data-sets. The good point of this method is that the quality evaluation method is independent of the data sources and coordinate systems because the co-registration can finally transform the verification and input datasets into the same coordinate system.…”

Evaluation of Model Recognition for Grammar-Based Automatic 3d Building Model Reconstruction

“…Much work has been already done on the semi-automatic or automatic approaches for building reconstruction from terrestrial laser scanning data, especially using grammar-based methods (Brenner, 2005;Milde et al, 2008). However, in contrast to the huge amount of literature dealing with the method for building reconstruction, there are only a few references focusing on evaluation of the reconstructed building models (Akca et al, 2010). Evaluation process plays a significant role in building model reconstruction since it may give important information about deficiencies of a reconstruction approach and help improve the approach.…”