scholarly journals GEOMETRIC POINT QUALITY ASSESSMENT FOR THE AUTOMATED, MARKERLESS AND ROBUST REGISTRATION OF UNORDERED TLS POINT CLOUDS

2015 ◽  
Vol II-3/W5 ◽  
pp. 89-96 ◽  
Author(s):  
M. Weinmann ◽  
B. Jutzi
Keyword(s):  
3D Reconstruction ◽  
Laser Scanning ◽  
Incidence Angle ◽  
Point Clouds ◽  
Urban Environments ◽  
Coordinate Frame ◽  
Consistency Check ◽  
Geometric Point ◽  
Feature Correspondences ◽  
Urban Accessibility

The faithful 3D reconstruction of urban environments is an important prerequisite for tasks such as city modeling, scene interpretation or urban accessibility analysis. Typically, a dense and accurate 3D reconstruction is acquired with terrestrial laser scanning (TLS) systems by capturing several scans from different locations, and the respective point clouds have to be aligned correctly in a common coordinate frame. In this paper, we present an accurate and robust method for a keypoint-based registration of unordered point clouds via projective scan matching. Thereby, we involve a consistency check which removes unreliable feature correspondences and thus increases the ratio of inlier correspondences which, in turn, leads to a faster convergence of the RANSAC algorithm towards a suitable solution. This consistency check is fully generic and it not only favors geometrically smooth object surfaces, but also those object surfaces with a reasonable incidence angle. We demonstrate the performance of the proposed methodology on a standard TLS benchmark dataset and show that a highly accurate and robust registration may be achieved in a fully automatic manner without using artificial markers.

scholarly journals Virtual Disassembling of Historical Edifices: Experiments and Assessments of an Automatic Approach for Classifying Multi-Scalar Point Clouds into Architectural Elements

Sensors ◽  
2020 ◽  
Vol 20 (8) ◽  
pp. 2161 ◽  
Author(s):  
Arnadi Murtiyoso ◽  
Pierre Grussenmeyer
Keyword(s):  
Point Cloud ◽  
Laser Scanning ◽  
Semantic Annotation ◽  
Semantic Segmentation ◽  
Point Clouds ◽  
Training Data ◽  
Algorithmic Approach ◽  
Architectural Elements ◽  
Semantic Labeling ◽  
Geometric Point

3D heritage documentation has seen a surge in the past decade due to developments in reality-based 3D recording techniques. Several methods such as photogrammetry and laser scanning are becoming ubiquitous amongst architects, archaeologists, surveyors, and conservators. The main result of these methods is a 3D representation of the object in the form of point clouds. However, a solely geometric point cloud is often insufficient for further analysis, monitoring, and model predicting of the heritage object. The semantic annotation of point clouds remains an interesting research topic since traditionally it requires manual labeling and therefore a lot of time and resources. This paper proposes an automated pipeline to segment and classify multi-scalar point clouds in the case of heritage object. This is done in order to perform multi-level segmentation from the scale of a historical neighborhood up until that of architectural elements, specifically pillars and beams. The proposed workflow involves an algorithmic approach in the form of a toolbox which includes various functions covering the semantic segmentation of large point clouds into smaller, more manageable and semantically labeled clusters. The first part of the workflow will explain the segmentation and semantic labeling of heritage complexes into individual buildings, while a second part will discuss the use of the same toolbox to segment the resulting buildings further into architectural elements. The toolbox was tested on several historical buildings and showed promising results. The ultimate intention of the project is to help the manual point cloud labeling, especially when confronted with the large training data requirements of machine learning-based algorithms.

scholarly journals AN ENERGY-BASED APPROACH FOR DETECTION AND CHARACTERIZATION OF SUBTLE ENTITIES WITHIN LASER SCANNING POINT-CLOUDS

2016 ◽  
Vol XLI-B3 ◽  
pp. 167-171
Author(s):  
Reuma Arav ◽  
Sagi Filin
Keyword(s):  
Laser Scanning ◽  
Optimization Problem ◽  
Point Clouds ◽  
Urban Environments ◽  
Natural Environments ◽  
Detection Scheme ◽  
Airborne Laser ◽  
The Common ◽  
Geomorphological Features

Airborne laser scans present an optimal tool to describe geomorphological features in natural environments. However, a challenge arises in the detection of such phenomena, as they are embedded in the topography, tend to blend into their surroundings and leave only a subtle signature within the data. Most object-recognition studies address mainly urban environments and follow a general pipeline where the data are partitioned into segments with uniform properties. These approaches are restricted to man-made domain and are capable to handle limited features that answer a well-defined geometric form. As natural environments present a more complex set of features, the common interpretation of the data is still manual at large. In this paper, we propose a data-aware detection scheme, unbound to specific domains or shapes. We define the recognition question as an energy optimization problem, solved by variational means. Our approach, based on the level-set method, characterizes geometrically local surfaces within the data, and uses these characteristics as potential field for minimization. The main advantage here is that it allows topological changes of the evolving curves, such as merging and breaking. We demonstrate the proposed methodology on the detection of collapse sinkholes.

scholarly journals Terrestrial laser scanning in monitoring of anthropogenic objects

2017 ◽  
Vol 66 (2) ◽  
pp. 347-364
Author(s):  
Janina Zaczek-Peplinska ◽  
Maria Kowalska
Keyword(s):  
Laser Scanning ◽  
Incidence Angle ◽  
Geometric Analysis ◽  
Laser Scanner ◽  
Terrestrial Laser Scanning ◽  
Point Clouds ◽  
Angle Of Incidence ◽  
Point Distribution ◽  
Common Coordinate System ◽  
Cloud Of Points

Abstract The registered xyz coordinates in the form of a point cloud captured by terrestrial laser scanner and the intensity values (I) assigned to them make it possible to perform geometric and spectral analyses. Comparison of point clouds registered in different time periods requires conversion of the data to a common coordinate system and proper data selection is necessary. Factors like point distribution dependant on the distance between the scanner and the surveyed surface, angle of incidence, tasked scan’s density and intensity value have to be taken into consideration. A prerequisite for running a correct analysis of the obtained point clouds registered during periodic measurements using a laser scanner is the ability to determine the quality and accuracy of the analysed data. The article presents a concept of spectral data adjustment based on geometric analysis of a surface as well as examples of geometric analyses integrating geometric and physical data in one cloud of points: cloud point coordinates, recorded intensity values, and thermal images of an object. The experiments described here show multiple possibilities of usage of terrestrial laser scanning data and display the necessity of using multi-aspect and multi-source analyses in anthropogenic object monitoring. The article presents examples of multisource data analyses with regard to Intensity value correction due to the beam’s incidence angle. The measurements were performed using a Leica Nova MS50 scanning total station, Z+F Imager 5010 scanner and the integrated Z+F T-Cam thermal camera.

scholarly journals AN EFFICIENT DEEP LEARNING APPROACH FOR GROUND POINT FILTERING IN AERIAL LASER SCANNING POINT CLOUDS

2021 ◽  
Vol XLIII-B1-2021 ◽  
pp. 31-38
Author(s):  
A. Nurunnabi ◽  
F. N. Teferle ◽  
J. Li ◽  
R. C. Lindenbergh ◽  
A. Hunegnaw
Keyword(s):  
Deep Learning ◽  
Point Cloud ◽  
Laser Scanning ◽  
Three Dimensional ◽  
Ground Surface ◽  
Point Clouds ◽  
Urban Environments ◽  
Training Data ◽  
Ground Point ◽  
End To End

Abstract. Ground surface extraction is one of the classic tasks in airborne laser scanning (ALS) point cloud processing that is used for three-dimensional (3D) city modelling, infrastructure health monitoring, and disaster management. Many methods have been developed over the last three decades. Recently, Deep Learning (DL) has become the most dominant technique for 3D point cloud classification. DL methods used for classification can be categorized into end-to-end and non end-to-end approaches. One of the main challenges of using supervised DL approaches is getting a sufficient amount of training data. The main advantage of using a supervised non end-to-end approach is that it requires less training data. This paper introduces a novel local feature-based non end-to-end DL algorithm that generates a binary classifier for ground point filtering. It studies feature relevance, and investigates three models that are different combinations of features. This method is free from the limitations of point clouds’ irregular data structure and varying data density, which is the biggest challenge for using the elegant convolutional neural network. The new algorithm does not require transforming data into regular 3D voxel grids or any rasterization. The performance of the new method has been demonstrated through two ALS datasets covering urban environments. The method successfully labels ground and non-ground points in the presence of steep slopes and height discontinuity in the terrain. Experiments in this paper show that the algorithm achieves around 97% in both F1-score and model accuracy for ground point labelling.

scholarly journals A VOXEL-BASED METADATA STRUCTURE FOR CHANGE DETECTION IN POINT CLOUDS OF LARGE-SCALE URBAN AREAS

2018 ◽  
Vol IV-2 ◽  
pp. 97-104
Author(s):  
J. Gehrung ◽  
M. Hebel ◽  
M. Arens ◽  
U. Stilla
Keyword(s):  
Change Detection ◽  
Urban Areas ◽  
Laser Scanning ◽  
Large Scale ◽  
Point Clouds ◽  
Urban Environments ◽  
Detection Methods ◽  
Planar Surfaces ◽  
Occupancy Grids ◽  
Speed Up

Mobile laser scanning has not only the potential to create detailed representations of urban environments, but also to determine changes up to a very detailed level. An environment representation for change detection in large scale urban environments based on point clouds has drawbacks in terms of memory scalability. Volumes, however, are a promising building block for memory efficient change detection methods. The challenge of working with 3D occupancy grids is that the usual raycasting-based methods applied for their generation lead to artifacts caused by the traversal of unfavorable discretized space. These artifacts have the potential to distort the state of voxels in close proximity to planar structures. In this work we propose a raycasting approach that utilizes knowledge about planar surfaces to completely prevent this kind of artifacts. To demonstrate the capabilities of our approach, a method for the iterative volumetric approximation of point clouds that allows to speed up the raycasting by 36 percent is proposed.

scholarly journals FINDING A GOOD FEATURE DETECTOR-DESCRIPTOR COMBINATION FOR THE 2D KEYPOINT-BASED REGISTRATION OF TLS POINT CLOUDS

2015 ◽  
Vol II-3/W5 ◽  
pp. 121-128 ◽  
Author(s):  
S. Urban ◽  
M. Weinmann
Keyword(s):  
Point Cloud ◽  
Laser Scanning ◽  
Point Clouds ◽  
Scale Invariant ◽  
Point Cloud Registration ◽  
Speeded Up Robust Features ◽  
Extensive Evaluation ◽  
Feature Correspondences ◽  
Intensity Images ◽  
Keypoint Detector

The automatic and accurate registration of terrestrial laser scanning (TLS) data is a topic of great interest in the domains of city modeling, construction surveying or cultural heritage. While numerous of the most recent approaches focus on keypoint-based point cloud registration relying on forward-projected 2D keypoints detected in panoramic intensity images, little attention has been paid to the selection of appropriate keypoint detector-descriptor combinations. Instead, keypoints are commonly detected and described by applying well-known methods such as the Scale Invariant Feature Transform (SIFT) or Speeded-Up Robust Features (SURF). In this paper, we present a framework for evaluating the influence of different keypoint detector-descriptor combinations on the results of point cloud registration. For this purpose, we involve five different approaches for extracting local features from the panoramic intensity images and exploit the range information of putative feature correspondences in order to define bearing vectors which, in turn, may be exploited to transfer the task of point cloud registration from the object space to the observation space. With an extensive evaluation of our framework on a standard benchmark TLS dataset, we clearly demonstrate that replacing SIFT and SURF detectors and descriptors by more recent approaches significantly alleviates point cloud registration in terms of accuracy, efficiency and robustness.

scholarly journals AUTOMATIC 3D RECONSTRUCTION OF COMPLEX BUILDINGS FROM INCOMPLETE POINT CLOUDS WITH TOPOLOGICAL-RELATION CONSTRAINTS

2020 ◽  
Vol V-5-2020 ◽  
pp. 85-92
Author(s):  
Y. Li ◽  
B. Wu
Keyword(s):  
3D Reconstruction ◽  
Laser Scanning ◽  
Large Scale ◽  
Point Clouds ◽  
3D Models ◽  
Automatic Generation ◽  
Topological Relation ◽  
Architectural Styles ◽  
Topological Errors ◽  
Polyhedral Cells

Abstract. Automatic 3D building reconstruction from laser scanning or photogrammetric point clouds has gained increasing attention in the past two decades. Although many efforts have been made, the complexity of buildings and incompletion of point clouds, i.e., data missing, still make it a challenging task for automatic 3D reconstruction of buildings in large-scale urban scenes with various architectural styles. This paper presents an innovative approach for automatic generation of 3D models of complex buildings from even incomplete point clouds. The approach first decomposes the 3D space into multiple space units, including 3D polyhedral cells, facets and edges, where the facets and edges are also encoded with topological-relation constraints. Then, the units and constraints are used together to approximate the buildings. On one hand, by extracting facets from 3D cells and further extracting edges from facets, this approach simplifies complicated topological computations. On the other hand, because this approach models buildings on the basis of polyhedral cells, it can guarantee that the models are manifold and watertight and avoid correcting topological errors. A challenging dataset containing 105 buildings acquired in Central, Hong Kong, was used to evaluate the performance of the proposed approach. The results were compared with two previous methods and the comparisons suggested that the proposed approach outperforms other methods in terms of robustness, regularity, and accuracy of the models, with an average root-mean-square error of less than 0.9 m. The proposed approach is of significance for automatic 3D modelling of buildings for urban applications.

scholarly journals How Much of a Historic Town Can Be Mapped by a Terrestrial Laser Scanner within a Working Day? - A Single Touch Workflow

2017 ◽  
Vol 1 (2) ◽  
pp. 239-250
Author(s):  
Christoph Fürst ◽  
Nikolaus Studnicka ◽  
Martin Pfennigbauer
Keyword(s):  
Laser Scanning ◽  
Laser Scanner ◽  
Terrestrial Laser Scanning ◽  
Point Clouds ◽  
Urban Environments ◽  
Terrestrial Laser Scanner ◽  
Single Operator ◽  
Working Day ◽  
The One ◽  
3D Information

Downtown Vienna with its world-famous cultural sites and architectural features is most definitely worth conservation. One way to archive at least a digital 3D imprint is laser scanning. While urban mapping with airborne or mobile laser scanning is fast and efficient, the resulting point clouds might not have the required resolution or might experience gaps due to shadowing. Terrestrial laser scanning has the potential to overcome these limitations. However, it has long been considered time-consuming and labour-intensive both while capturing and also while processing the data.In order to challenge this, we performed a field test with the new RIEGL VZ-400i terrestrial laser scanner. For eight hours, in the night from 2nd to 3rd of June 2016, one single operator employed the instrument throughout the city center of Vienna. He managed to take 514 high-resolution laser scans with approximately 9m between the scan positions.The data acquired in the course of this test impressively demonstrates the potential of state-of-the-art terrestrial laser scanning to preserve detailed 3D-information of urban environments within limited amount of time. This paper describes the complete workflow from the one touch operation in the field up to the automatic registration process of the collected laser scans.     

scholarly journals A FRAMEWORK FOR VOXEL-BASED GLOBAL SCALE MODELING OF URBAN ENVIRONMENTS

2016 ◽  
Vol XLII-2/W1 ◽  
pp. 45-51 ◽  
Author(s):  
Joachim Gehrung ◽  
Marcus Hebel ◽  
Michael Arens ◽  
Uwe Stilla
Keyword(s):  
Laser Scanning ◽  
Large Scale ◽  
Point Clouds ◽  
Global Scale ◽  
Urban Environments ◽  
Scale Modeling ◽  
Volumetric Models ◽  
University Of Munich ◽  
Urban Models ◽  
The Right

The generation of 3D city models is a very active field of research. Modeling environments as point clouds may be fast, but has disadvantages. These are easily solvable by using volumetric representations, especially when considering selective data acquisition, change detection and fast changing environments. Therefore, this paper proposes a framework for the volumetric modeling and visualization of large scale urban environments. Beside an architecture and the right mix of algorithms for the task, two compression strategies for volumetric models as well as a data quality based approach for the import of range measurements are proposed. The capabilities of the framework are shown on a mobile laser scanning dataset of the Technical University of Munich. Furthermore the loss of the compression techniques is evaluated and their memory consumption is compared to that of raw point clouds. The presented results show that generation, storage and real-time rendering of even large urban models are feasible, even with off-the-shelf hardware.

scholarly journals INPAINTING OCCLUSION HOLES IN 3D BUILT ENVIRONMENT POINT CLOUDS

2019 ◽  
Vol XLII-2/W17 ◽  
pp. 393-398
Author(s):  
P. Väänänen ◽  
V. Lehtola
Keyword(s):  
Neural Networks ◽  
Missing Data ◽  
Built Environment ◽  
3D Reconstruction ◽  
Laser Scanning ◽  
3D Visualization ◽  
Terrestrial Laser Scanning ◽  
Point Clouds

Abstract. Point clouds obtained from mobile and terrestrial laser scanning are imperfect as data is typically missing due to occlusions. This problem is often encountered in 3D reconstruction and is especially troublesome for 3D visualization applications. The missing data may be recovered by intensifying the scanning mission, which may be expensive, or to some extent, by computational means. Here, we present an inpainting technique that covers these occlusion holes in 3D built environment point clouds. The proposed technique uses two neural networks with an identical architecture, applied separately for geometry and colors.

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