scholarly journals Object Recognition, Segmentation, and Classification of Mobile Laser Scanning Point Clouds: A State of the Art Review

Sensors ◽  
2019 ◽  
Vol 19 (4) ◽  
pp. 810 ◽  
Author(s):  
Erzhuo Che ◽  
Jaehoon Jung ◽  
Michael Olsen
Keyword(s):  
Feature Extraction ◽  
Object Recognition ◽  
Data Processing ◽  
Point Cloud ◽  
Laser Scanning ◽  
State Of The Art ◽  
Point Clouds ◽  
Wide Range ◽  
Benchmark Datasets ◽  
The Impact

Mobile Laser Scanning (MLS) is a versatile remote sensing technology based on Light Detection and Ranging (lidar) technology that has been utilized for a wide range of applications. Several previous reviews focused on applications or characteristics of these systems exist in the literature, however, reviews of the many innovative data processing strategies described in the literature have not been conducted in sufficient depth. To this end, we review and summarize the state of the art for MLS data processing approaches, including feature extraction, segmentation, object recognition, and classification. In this review, we first discuss the impact of the scene type to the development of an MLS data processing method. Then, where appropriate, we describe relevant generalized algorithms for feature extraction and segmentation that are applicable to and implemented in many processing approaches. The methods for object recognition and point cloud classification are further reviewed including both the general concepts as well as technical details. In addition, available benchmark datasets for object recognition and classification are summarized. Further, the current limitations and challenges that a significant portion of point cloud processing techniques face are discussed. This review concludes with our future outlook of the trends and opportunities of MLS data processing algorithms and applications.

scholarly journals Combined Use of Terrestrial Laser Scanning and UAV Photogrammetry in Mapping Alpine Terrain

2019 ◽  
Vol 11 (18) ◽  
pp. 2154 ◽  
Author(s):  
Ján Šašak ◽  
Michal Gallay ◽  
Ján Kaňuk ◽  
Jaroslav Hofierka ◽  
Jozef Minár
Keyword(s):  
High Resolution ◽  
Point Cloud ◽  
Laser Scanning ◽  
Terrestrial Laser Scanning ◽  
Point Clouds ◽  
Mutual Orientation ◽  
Close Range Photogrammetry ◽  
Combined Use ◽  
Wide Range ◽  
Close Range

Airborne and terrestrial laser scanning and close-range photogrammetry are frequently used for very high-resolution mapping of land surface. These techniques require a good strategy of mapping to provide full visibility of all areas otherwise the resulting data will contain areas with no data (data shadows). Especially, deglaciated rugged alpine terrain with abundant large boulders, vertical rock faces and polished roche-moutones surfaces complicated by poor accessibility for terrestrial mapping are still a challenge. In this paper, we present a novel methodological approach based on a combined use of terrestrial laser scanning (TLS) and close-range photogrammetry from an unmanned aerial vehicle (UAV) for generating a high-resolution point cloud and digital elevation model (DEM) of a complex alpine terrain. The approach is demonstrated using a small study area in the upper part of a deglaciated valley in the Tatry Mountains, Slovakia. The more accurate TLS point cloud was supplemented by the UAV point cloud in areas with insufficient TLS data coverage. The accuracy of the iterative closest point adjustment of the UAV and TLS point clouds was in the order of several centimeters but standard deviation of the mutual orientation of TLS scans was in the order of millimeters. The generated high-resolution DEM was compared to SRTM DEM, TanDEM-X and national DMR3 DEM products confirming an excellent applicability in a wide range of geomorphologic applications.

scholarly journals A comprehensive study of the rate-distortion performance in MPEG point cloud compression

2019 ◽  
Vol 8 ◽  
Author(s):  
Evangelos Alexiou ◽  
Irene Viola ◽  
Tomás M. Borges ◽  
Tiago A. Fonseca ◽  
Ricardo L. de Queiroz ◽  
...  
Keyword(s):  
Point Cloud ◽  
State Of The Art ◽  
Subjective Evaluation ◽  
Rate Distortion ◽  
Point Clouds ◽  
Current Approach ◽  
User Engagement ◽  
Viable Solution ◽  
Wide Range ◽  
Objective Quality

Abstract Recent trends in multimedia technologies indicate the need for richer imaging modalities to increase user engagement with the content. Among other alternatives, point clouds denote a viable solution that offers an immersive content representation, as witnessed by current activities in JPEG and MPEG standardization committees. As a result of such efforts, MPEG is at the final stages of drafting an emerging standard for point cloud compression, which we consider as the state-of-the-art. In this study, the entire set of encoders that have been developed in the MPEG committee are assessed through an extensive and rigorous analysis of quality. We initially focus on the assessment of encoding configurations that have been defined by experts in MPEG for their core experiments. Then, two additional experiments are designed and carried to address some of the identified limitations of current approach. As part of the study, state-of-the-art objective quality metrics are benchmarked to assess their capability to predict visual quality of point clouds under a wide range of radically different compression artifacts. To carry the subjective evaluation experiments, a web-based renderer is developed and described. The subjective and objective quality scores along with the rendering software are made publicly available, to facilitate and promote research on the field.

scholarly journals TIF-Reg: Point Cloud Registration with Transform-Invariant Features in SE(3)

Sensors ◽  
2021 ◽  
Vol 21 (17) ◽  
pp. 5778
Author(s):  
Baifan Chen ◽  
Hong Chen ◽  
Baojun Song ◽  
Grace Gong
Keyword(s):  
Feature Extraction ◽  
Point Cloud ◽  
Point Clouds ◽  
Rigid Transformation ◽  
Point Cloud Registration ◽  
Registration Method ◽  
Invariant Features ◽  
Invariant Feature ◽  
Deep Feature ◽  
Wide Range

Three-dimensional point cloud registration (PCReg) has a wide range of applications in computer vision, 3D reconstruction and medical fields. Although numerous advances have been achieved in the field of point cloud registration in recent years, large-scale rigid transformation is a problem that most algorithms still cannot effectively handle. To solve this problem, we propose a point cloud registration method based on learning and transform-invariant features (TIF-Reg). Our algorithm includes four modules, which are the transform-invariant feature extraction module, deep feature embedding module, corresponding point generation module and decoupled singular value decomposition (SVD) module. In the transform-invariant feature extraction module, we design TIF in SE(3) (which means the 3D rigid transformation space) which contains a triangular feature and local density feature for points. It fully exploits the transformation invariance of point clouds, making the algorithm highly robust to rigid transformation. The deep feature embedding module embeds TIF into a high-dimension space using a deep neural network, further improving the expression ability of features. The corresponding point cloud is generated using an attention mechanism in the corresponding point generation module, and the final transformation for registration is calculated in the decoupled SVD module. In an experiment, we first train and evaluate the TIF-Reg method on the ModelNet40 dataset. The results show that our method keeps the root mean squared error (RMSE) of rotation within 0.5∘ and the RMSE of translation error close to 0 m, even when the rotation is up to [−180∘, 180∘] or the translation is up to [−20 m, 20 m]. We also test the generalization of our method on the TUM3D dataset using the model trained on Modelnet40. The results show that our method’s errors are close to the experimental results on Modelnet40, which verifies the good generalization ability of our method. All experiments prove that the proposed method is superior to state-of-the-art PCReg algorithms in terms of accuracy and complexity.

scholarly journals INDOOR MAPPING OF A COMPLEX CULTURAL HERITAGE SCENE USING TLS AND HMLS LASER SCANNING

2021 ◽  
Vol XLIII-B2-2021 ◽  
pp. 605-612
Author(s):  
V. E. Oniga ◽  
A. I. Breaban ◽  
E. I. Alexe ◽  
C. Văsii
Keyword(s):  
Standard Deviation ◽  
Cultural Heritage ◽  
Long Range ◽  
Interior Design ◽  
Point Cloud ◽  
Laser Scanning ◽  
Laser Scanner ◽  
Point Clouds ◽  
Acquisition Time ◽  
Wide Range

Abstract. Indoor mapping and modelling is an important research subject with application in a wide range of domains including interior design, real estate, cultural heritage conservation and restoration. There are multiple sensors applicable for 3D indoor modelling, but the laser scanning technique is frequently used because of the acquisition time, detailed information and accuracy. In this paper, the efficiency of the Maptek I-Site 8820 terrestrial scanner, which is a long-range laser scanner and the accuracy of a HMLS point cloud acquired with a mobile scanner, namely GeoSlam Zeb Horizon were tested for indoor mapping. Aula Magna “Carmen Silva” of the “Gheorghe Asachi” Technical University of Iasi is studied in the current paper since the auditorium interior creates a distinct environment that combines complex geometric structures with architectural lighting and for preserving its great cultural value, the monument has a national historical significance. The registration process of the TLS point clouds was done using two methods: a semi-automatic one with artificial targets and a completely automatic one, based on Iterative Closest Point (ICP) algorithm. The resulted TLS point cloud was analysed in relation to the HMLS point cloud by computing the M3C2 (Multiscale Model to Model Cloud Comparison), obtaining a standard deviation of 2.1 cm and by investigating the Hausdorff distances from which resulted a standard deviation (σ) of 1.6 cm. Cross-sections have been extracted from the HMLS and TLS point clouds and after comparing the sections, 80% of the sigma values are less or equal to 1 cm. The results show high potential of using HMLS and also a long-range laser scanner for 3D modelling of complex scenes, the occlusion effect in the case of TLS being only 5% of the scanned area.

Structural assessment using terrestrial laser scanning point clouds

2021 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Linh Truong-Hong ◽  
Roderik Lindenbergh ◽  
Thu Anh Nguyen
Keyword(s):  
Point Cloud ◽  
Laser Scanning ◽  
Noisy Data ◽  
Point Clouds ◽  
Reference Surface ◽  
Content Type ◽  
Mixed Pixels ◽  
Data Points ◽  
The Impact

PurposeTerrestrial laser scanning (TLS) point clouds have been widely used in deformation measurement for structures. However, reliability and accuracy of resulting deformation estimation strongly depends on quality of each step of a workflow, which are not fully addressed. This study aims to give insight error of these steps, and results of the study would be guidelines for a practical community to either develop a new workflow or refine an existing one of deformation estimation based on TLS point clouds. Thus, the main contributions of the paper are investigating point cloud registration error affecting resulting deformation estimation, identifying an appropriate segmentation method used to extract data points of a deformed surface, investigating a methodology to determine an un-deformed or a reference surface for estimating deformation, and proposing a methodology to minimize the impact of outlier, noisy data and/or mixed pixels on deformation estimation.Design/methodology/approachIn practice, the quality of data point clouds and of surface extraction strongly impacts on resulting deformation estimation based on laser scanning point clouds, which can cause an incorrect decision on the state of the structure if uncertainty is available. In an effort to have more comprehensive insight into those impacts, this study addresses four issues: data errors due to data registration from multiple scanning stations (Issue 1), methods used to extract point clouds of structure surfaces (Issue 2), selection of the reference surface Sref to measure deformation (Issue 3), and available outlier and/or mixed pixels (Issue 4). This investigation demonstrates through estimating deformation of the bridge abutment, building and an oil storage tank.FindingsThe study shows that both random sample consensus (RANSAC) and region growing–based methods [a cell-based/voxel-based region growing (CRG/VRG)] can be extracted data points of surfaces, but RANSAC is only applicable for a primary primitive surface (e.g. a plane in this study) subjected to a small deformation (case study 2 and 3) and cannot eliminate mixed pixels. On another hand, CRG and VRG impose a suitable method applied for deformed, free-form surfaces. In addition, in practice, a reference surface of a structure is mostly not available. The use of a fitting plane based on a point cloud of a current surface would cause unrealistic and inaccurate deformation because outlier data points and data points of damaged areas affect an accuracy of the fitting plane. This study would recommend the use of a reference surface determined based on a design concept/specification. A smoothing method with a spatial interval can be effectively minimize, negative impact of outlier, noisy data and/or mixed pixels on deformation estimation.Research limitations/implicationsDue to difficulty in logistics, an independent measurement cannot be established to assess the deformation accuracy based on TLS data point cloud in the case studies of this research. However, common laser scanners using the time-of-flight or phase-shift principle provide point clouds with accuracy in the order of 1–6 mm, while the point clouds of triangulation scanners have sub-millimetre accuracy.Practical implicationsThis study aims to give insight error of these steps, and the results of the study would be guidelines for a practical community to either develop a new workflow or refine an existing one of deformation estimation based on TLS point clouds.Social implicationsThe results of this study would provide guidelines for a practical community to either develop a new workflow or refine an existing one of deformation estimation based on TLS point clouds. A low-cost method can be applied for deformation analysis of the structure.Originality/valueAlthough a large amount of the studies used laser scanning to measure structure deformation in the last two decades, the methods mainly applied were to measure change between two states (or epochs) of the structure surface and focused on quantifying deformation-based TLS point clouds. Those studies proved that a laser scanner could be an alternative unit to acquire spatial information for deformation monitoring. However, there are still challenges in establishing an appropriate procedure to collect a high quality of point clouds and develop methods to interpret the point clouds to obtain reliable and accurate deformation, when uncertainty, including data quality and reference information, is available. Therefore, this study demonstrates the impact of data quality in a term of point cloud registration error, selected methods for extracting point clouds of surfaces, identifying reference information, and available outlier, noisy data and/or mixed pixels on deformation estimation.

scholarly journals Landscape monitoring of post-industrial areas using LiDAR and GIS technology

2015 ◽  
Vol 64 (1) ◽  
pp. 125-137 ◽  
Author(s):  
Piotr Wężyk ◽  
Marta Szostak ◽  
Wojciech Krzaklewski ◽  
Marek Pająk ◽  
Marcin Pierzchalski ◽  
...  
Keyword(s):  
Point Cloud ◽  
Laser Scanning ◽  
Point Clouds ◽  
Slope Aspect ◽  
Gis Technology ◽  
Industrial Areas ◽  
Cloud Data ◽  
Post Industrial ◽  
The Impact ◽  
Spoil Heaps

Abstract The quarrying industry is changing the local landscape, forming deep open pits and spoil heaps in close proximity to them, especially lignite mines. The impact can include toxic soil material (low pH, heavy metals, oxidations etc.) which is the basis for further reclamation and afforestation. Forests that stand on spoil heaps have very different growth conditions because of the relief (slope, aspect, wind and rainfall shadows, supply of solar energy, etc.) and type of soil that is deposited. Airborne laser scanning (ALS) technology deliver point clouds (XYZ) and derivatives as raster height models (DTM, DSM, nDSM=CHM) which allow the reception of selected 2D and 3D forest parameters (e.g. height, base of the crown, cover, density, volume, biomass, etc). The automation of ALS point cloud processing and integrating the results into GIS helps forest managers to take appropriate decisions on silvicultural treatments in areas with failed plantations (toxic soil, droughts on south-facing slopes; landslides, etc.) or as regular maintenance. The ISOK country-wide project ongoing in Poland will soon deliver ALS point cloud data which can be successfully used for the monitoring and management of many thousands of hectares of destroyed post-industrial areas which according to the law, have to be afforested and transferred back to the State Forest.

scholarly journals EXPLORING CROSS-CITY SEMANTIC SEGMENTATION OF ALS POINT CLOUDS

2021 ◽  
Vol XLIII-B2-2021 ◽  
pp. 247-254
Author(s):  
Y. Xie ◽  
K. Schindler ◽  
J. Tian ◽  
X. X. Zhu
Keyword(s):  
Point Cloud ◽  
Laser Scanning ◽  
Data Augmentation ◽  
Poor Performance ◽  
Semantic Segmentation ◽  
Point Clouds ◽  
Training Data ◽  
The Impact ◽  
Cloud Density ◽  
Deep Coral

Abstract. Deep learning models achieve excellent semantic segmentation results for airborne laser scanning (ALS) point clouds, if sufficient training data are provided. Increasing amounts of annotated data are becoming publicly available thanks to contributors from all over the world. However, models trained on a specific dataset typically exhibit poor performance on other datasets. I.e., there are significant domain shifts, as data captured in different environments or by distinct sensors have different distributions. In this work, we study this domain shift and potential strategies to mitigate it, using two popular ALS datasets: the ISPRS Vaihingen benchmark from Germany and the LASDU benchmark from China. We compare different training strategies for cross-city ALS point cloud semantic segmentation. In our experiments, we analyse three factors that may lead to domain shift and affect the learning: point cloud density, LiDAR intensity, and the role of data augmentation. Moreover, we evaluate a well-known standard method of domain adaptation, deep CORAL (Sun and Saenko, 2016). In our experiments, adapting the point cloud density and appropriate data augmentation both help to reduce the domain gap and improve segmentation accuracy. On the contrary, intensity features can bring an improvement within a dataset, but deteriorate the generalisation across datasets. Deep CORAL does not further improve the accuracy over the simple adaptation of density and data augmentation, although it can mitigate the impact of improperly chosen point density, intensity features, and further dataset biases like lack of diversity.

Tracking of secondary and temporary objects in structural concrete work

2014 ◽  
Vol 14 (2) ◽  
pp. 145-167 ◽  
Author(s):  
Yelda Turkan ◽  
Frédéric Bosché ◽  
Carl T. Haas ◽  
Ralph Haas
Keyword(s):  
Object Recognition ◽  
Laser Scanning ◽  
Real Life ◽  
Point Clouds ◽  
Earned Value ◽  
Structural Concrete ◽  
Content Type ◽  
3D Point Clouds ◽  
Bim Object ◽  
The Impact

Purpose – Previous research has shown that “Scan-vs-BIM” object recognition systems, which fuse three dimensional (3D) point clouds from terrestrial laser scanning (TLS) or digital photogrammetry with 4D project building information models (BIM), provide valuable information for tracking construction works. However, until now, the potential of these systems has been demonstrated for tracking progress of permanent structural works only; no work has been reported yet on tracking secondary or temporary structures. For structural concrete work, temporary structures include formwork, scaffolding and shoring, while secondary components include rebar. Together, they constitute most of the earned value in concrete work. The impact of tracking secondary and temporary objects would thus be added veracity and detail to earned value calculations, and subsequently better project control and performance. The paper aims to discuss these issues. Design/methodology/approach – Two techniques for recognizing concrete construction secondary and temporary objects in TLS point clouds are implemented and tested using real-life data collected from a reinforced concrete building construction site. Both techniques represent significant innovative extensions of existing “Scan-vs-BIM” object recognition frameworks. Findings – The experimental results show that it is feasible to recognise secondary and temporary objects in TLS point clouds with good accuracy using the two novel techniques; but it is envisaged that superior results could be achieved by using additional cues such as colour and 3D edge information. Originality/value – This article makes valuable contributions to the problem of detecting and tracking secondary and temporary objects in 3D point clouds. The power of Scan-vs-BIM object recognition approaches to address this problem is demonstrated, but their limitations are also highlighted.

scholarly journals COMPARISON OF SOFTWARE FOR AIRBORNE LASER SCANNING DATA PROCESSING IN SMART CITY APPLICATIONS

2019 ◽  
Vol XLII-5/W2 ◽  
pp. 9-13 ◽  
Author(s):  
V. Badenko ◽  
D. Zotov ◽  
N. Muromtseva ◽  
Y. Volkova ◽  
P. Chernov
Keyword(s):  
Data Processing ◽  
Smart City ◽  
Point Cloud ◽  
Laser Scanning ◽  
Point Clouds ◽  
Test Point ◽  
Airborne Laser Scanning ◽  
Cloud Processing ◽  
Airborne Laser ◽  
Erdas Imagine

<p><strong>Abstract.</strong> Problems of processing of point clouds of airborne laser scanning using different software for Smart City projects are considered. Results of comparison of suitable software on the base of a test point cloud are presented. For comparison we had chosen a criterion for how results of point cloud processing can be used in the smart city application. The following software were chosen for comparison: Erdas IMAGINE, ENVI Lidar, TerraSolid (without Terraslave), GlobalMapper, Autodesk InfraWorks. Comparison have been conducted in qualitative and quantitative terms. The results presented allowed us to create recommendations on the usage of specific software for airborne laser scanning data processing for Smart City projects.</p>

scholarly journals The Comparison of Stem Curve Accuracy Determined from Point Clouds Acquired by Different Terrestrial Remote Sensing Methods

2020 ◽  
Vol 12 (17) ◽  
pp. 2739 ◽  
Author(s):  
Milan Hunčaga ◽  
Juliána Chudá ◽  
Julián Tomaštík ◽  
Martina Slámová ◽  
Milan Koreň ◽  
...  
Keyword(s):  
Cross Sections ◽  
Point Cloud ◽  
Laser Scanning ◽  
Mean Squared Error ◽  
Point Clouds ◽  
Volume Estimation ◽  
Environment Modeling ◽  
Accuracy Of Measurements ◽  
Wide Range ◽  
Standing Trees

The knowledge of tree characteristics, especially the shape of standing trees, is important for living tree volume estimation, the computation of a wide range of forest stand features, and the evaluation of stand stability. Nowadays, nondestructive and accurate approaches to data collection in the forest environment are required. Therefore, the implementation of accurate point cloud-based information in the field of forest inventory has become increasingly required. We evaluated the stem curves of the lower part of standing trees (diameters at heights of 0.3 m to 8 m). The experimental data were acquired from three point cloud datasets, which were created through different approaches to three-dimensional (3D) environment modeling (varying in terms of data acquisition and processing time, acquisition costs, and processing complexity): terrestrial laser scanning (TLS), close-range photogrammetry (CRP), and handheld mobile laser scanning (HMLS) with a simultaneous localization and mapping algorithm (SLAM). Diameter estimation errors varied across heights of cross sections and methods. The average root mean squared error (RMSE) of all cross sections for the specific methods was 1.03 cm (TLS), 1.26 cm (HMLS), and 1.90 cm (CRP). TLS and CRP reached the lowest RMSE at a height of 1.3 m, while for HMLS, it was at the height of 8 m. Our findings demonstrated that the accuracy of measurements of the standing tree stem curve was comparable for the usability of all three devices in forestry practices.

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