Improving the Accuracy of Automatic Reconstruction of 3D Complex Buildings Models from Airborne Lidar Point Clouds

Marek Kulawiak,Zbigniew Lubniewski

doi:10.3390/rs12101643

Marek Kulawiak, Zbigniew Lubniewski

Open Access

https://doi.org/10.3390/rs12101643

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Journal: Remote Sensing	Publication Date: May 20, 2020
Citations: 15	License type: CC BY 4.0

Affiliation: Gdańsk University of Technology

Abstract

Due to high requirements of variety of 3D spatial data applications with respect to data amount and quality, automatized, efficient and reliable data acquisition and preprocessing methods are needed. The use of photogrammetry techniques—as well as the light detection and ranging (LiDAR) automatic scanners—are among attractive solutions. However, measurement data are in the form of unorganized point clouds, usually requiring transformation to higher order 3D models based on polygons or polyhedral surfaces, which is not a trivial process. The study presents a newly developed algorithm for correcting 3D point cloud data from airborne LiDAR surveys of regular 3D buildings. The proposed approach assumes the application of a sequence of operations resulting in 3D rasterization, i.e., creation and processing of a 3D regular grid representation of an object, prior to applying a regular Poisson surface reconstruction method. In order to verify the accuracy and quality of reconstructed objects for quantitative comparison with the obtained 3D models, high-quality ground truth models were used in the form of the meshes constructed from photogrammetric measurements and manually made using buildings architectural plans. The presented results show that applying the proposed algorithm positively influences the quality of the results and can be used in combination with existing surface reconstruction methods in order to generate more detailed 3D models from LiDAR scanning.

Highlights

Acquisition of the detailed, three-dimensional spatial data describing accurately the terrain and topographic objects is an important problem due to a wide range of applications, including: 1. Creating detailed three-dimensional topographic maps; 2
Both original and processed point cloud data were used as an input in the process of reconstructing three-dimensional meshes by using the Poisson surface reconstruction method implementation provided by MeshLab [40]
As the Poisson surface reconstruction method has a tendency to randomly create a large amount of redundant surfaces around the base of the reconstructed objects, before comparing the results, all output meshes were properly preprocessed in order to remove any elements located below the lowest point of the original input data

Summary

Introduction

Acquisition of the detailed, three-dimensional spatial data describing accurately the terrain and topographic objects is an important problem due to a wide range of applications, including: 1. Creating detailed three-dimensional topographic maps; 2. Acquisition of the detailed, three-dimensional spatial data describing accurately the terrain and topographic objects is an important problem due to a wide range of applications, including: 1. In the case of more complex and varied objects, obtaining satisfactory results is often much more difficult to achieve [31] In this context, the new approach to reconstruction of 3D models of topographic objects proposed in this study seems to be promising in terms of potential to improve the overall accuracy of the results. It is meant to be a robust method suitable for point clouds, such as airborne LiDAR data and is expected to create satisfactory results without the need of providing it with additional input data, unlike many other solutions [10,18,19]. The authors intent is to make this method simple to implement and adapt to different scenarios, which means that it should not rely on complex solutions such as machine learning [29]

Spatial Data

LiDAR Point Clouds

Reference Meshes

Results and Discussion

Conclusions