Comparison of the Selected State-Of-The-Art 3D Indoor Scanning and Point Cloud Generation Methods

Ville Lehtola,Aimad El Issaoui,Andreas Nüchter,Juho-Pekka Virtanen,Matti Vaaja,Eija Honkavaara,Anttoni Jaakkola,Harri Kaartinen,Antero Kukko,Tomi Rosnell,Lingli Zhu,Risto Kaijaluoto,Matti Kurkela,Paula Litkey,Juha Hyyppä

doi:10.3390/rs9080796

Ville Lehtola, Aimad El Issaoui + Show 13 more

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https://doi.org/10.3390/rs9080796

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Abstract

Accurate three-dimensional (3D) data from indoor spaces are of high importance for various applications in construction, indoor navigation and real estate management. Mobile scanning techniques are offering an efficient way to produce point clouds, but with a lower accuracy than the traditional terrestrial laser scanning (TLS). In this paper, we first tackle the problem of how the quality of a point cloud should be rigorously evaluated. Previous evaluations typically operate on some point cloud subset, using a manually-given length scale, which would perhaps describe the ranging precision or the properties of the environment. Instead, the metrics that we propose perform the quality evaluation to the full point cloud and over all of the length scales, revealing the method precision along with some possible problems related to the point clouds, such as outliers, over-completeness and misregistration. The proposed methods are used to evaluate the end product point clouds of some of the latest methods. In detail, point clouds are obtained from five commercial indoor mapping systems, Matterport, NavVis, Zebedee, Stencil and Leica Pegasus: Backpack, and three research prototypes, Aalto VILMA , FGI Slammer and the Würzburg backpack. These are compared against survey-grade TLS point clouds captured from three distinct test sites that each have different properties. Based on the presented experimental findings, we discuss the properties of the proposed metrics and the strengths and weaknesses of the above mapping systems and then suggest directions for future research.

Highlights

Demand for digital 3D models of building indoor spaces has been growing as the cost of producing one has reduced [1,2,3]
We first compute the full point cloud results with the proposed metric, interpret these and perform some more traditional rigidness and height elevation benchmarks based on point subsets to shed further understanding on the first interpretations
Scanning, by studying the properties of 3D point clouds provided by these mapping methods with a full-point cloud approach

Summary

Introduction

Demand for digital 3D models of building indoor spaces has been growing as the cost of producing one has reduced [1,2,3]. The use of these models can be characterized as two-fold, schematics and Remote Sens. Schematic model applications include creating as-built models for the planning and monitoring of construction processes and building conditions. Visually-appealing virtual models of cultural and historical sites enable people to experience them remotely, being advantageous for those sites that are too fragile for normal tourism. Decision making on city planning can be facilitated, and construction permit processes speed up with digitization

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