Remote Sensing

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35 pages, 13566 KiB

Open AccessArticle

3D Point Cloud Semantic Modelling: Integrated Framework for Indoor Spaces and Furniture

by Florent Poux, Romain Neuville, Gilles-Antoine Nys and Roland Billen

Remote Sens. 2018, 10(9), 1412; https://doi.org/10.3390/rs10091412 - 5 Sep 2018

Cited by 43 | Viewed by 14482

3D models derived from point clouds are useful in various shapes to optimize the trade-off between precision and geometric complexity. They are defined at different granularity levels according to each indoor situation. In this article, we present an integrated 3D semantic reconstruction framework [...] Read more.

3D models derived from point clouds are useful in various shapes to optimize the trade-off between precision and geometric complexity. They are defined at different granularity levels according to each indoor situation. In this article, we present an integrated 3D semantic reconstruction framework that leverages segmented point cloud data and domain ontologies. Our approach follows a part-to-whole conception which models a point cloud in parametric elements usable per instance and aggregated to obtain a global 3D model. We first extract analytic features, object relationships and contextual information to permit better object characterization. Then, we propose a multi-representation modelling mechanism augmented by automatic recognition and fitting from the 3D library ModelNet10 to provide the best candidates for several 3D scans of furniture. Finally, we combine every element to obtain a consistent indoor hybrid 3D model. The method allows a wide range of applications from interior navigation to virtual stores. Full article

(This article belongs to the Special Issue 3D Modelling from Point Clouds: Algorithms and Methods)

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19 pages, 19936 KiB

Open AccessFeature PaperArticle

Evaluating a Static Multibeam Sonar Scanner for 3D Surveys in Confined Underwater Environments

by Emmanuel Moisan, Pierre Charbonnier, Philippe Foucher, Pierre Grussenmeyer and Samuel Guillemin

Remote Sens. 2018, 10(9), 1395; https://doi.org/10.3390/rs10091395 - 1 Sep 2018

Cited by 8 | Viewed by 5249

Abstract

Mechanical Sonar Scanning (MSS) is a recent technology that allows sonar to be used for static measurements in the same way as Terrestrial Laser Scanners (TLS), which makes it an attractive tool for underwater infrastructure surveys. Nevertheless, the metrological capabilities of this type [...] Read more.

Mechanical Sonar Scanning (MSS) is a recent technology that allows sonar to be used for static measurements in the same way as Terrestrial Laser Scanners (TLS), which makes it an attractive tool for underwater infrastructure surveys. Nevertheless, the metrological capabilities of this type of device have been little explored in the literature, particularly in narrow and shallow environments. In this paper, we report on the experimental assessment of a recent MSS, the BlueView BV5000, in a lock. The 3D sonar scans performed with the system suspended from the surface are registered using an innovative algorithm that exploits external measurements from a total station and the symmetry of the structure. We review the different errors that impair sonar data, and compare the resulting point cloud to a TLS model that was acquired the day before, while the lock was completely emptied for maintenance. After correcting a tilt angle calibration error, the maximum difference is less than 10 cm, and the standard deviation is about 3 cm. Visual inspection shows that coarse defects of the masonry, such as stone lacks or cavities, can be detected in the MSS point cloud, while details smaller than 4 cm, e.g., damaged joints, are harder to notice. Full article

(This article belongs to the Special Issue 3D Modelling from Point Clouds: Algorithms and Methods)

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30 pages, 11273 KiB

Open AccessArticle

Reconstruction of Three-Dimensional (3D) Indoor Interiors with Multiple Stories via Comprehensive Segmentation

by Lin Li, Fei Su, Fan Yang, Haihong Zhu, Dalin Li, Xinkai Zuo, Feng Li, Yu Liu and Shen Ying

Remote Sens. 2018, 10(8), 1281; https://doi.org/10.3390/rs10081281 - 14 Aug 2018

Cited by 33 | Viewed by 6904

Abstract

The fast and stable reconstruction of building interiors from scanned point clouds has recently attracted considerable research interest. However, reconstructing long corridors and connected areas across multiple floors has emerged as a substantial challenge. This paper presents a comprehensive segmentation method for reconstructing [...] Read more.

The fast and stable reconstruction of building interiors from scanned point clouds has recently attracted considerable research interest. However, reconstructing long corridors and connected areas across multiple floors has emerged as a substantial challenge. This paper presents a comprehensive segmentation method for reconstructing a three-dimensional (3D) indoor structure with multiple stories. With this method, the over-segmentation that usually occurs in the reconstruction of long corridors in a complex indoor environment is overcome by morphologically eroding the floor space to segment rooms and by overlapping the segmented room-space with partitioned cells via extracted wall lines. Such segmentation ensures both the integrity of the room-space partitions and the geometric regularity of the rooms. For spaces across floors in a multistory building, a peak-nadir-peak strategy in the distribution of points along the z-axis is proposed in order to extract connected areas across multiple floors. A series of experimental tests while using seven real-world 3D scans and eight synthetic models of indoor environments show the effectiveness and feasibility of the proposed method. Full article

(This article belongs to the Special Issue 3D Modelling from Point Clouds: Algorithms and Methods)

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23 pages, 16287 KiB

Open AccessArticle

Automating Parameter Learning for Classifying Terrestrial LiDAR Point Cloud Using 2D Land Cover Maps

by Chen-Chieh Feng and Zhou Guo

Remote Sens. 2018, 10(8), 1192; https://doi.org/10.3390/rs10081192 - 30 Jul 2018

Cited by 12 | Viewed by 4498

Abstract

The automating classification of point clouds capturing urban scenes is critical for supporting applications that demand three-dimensional (3D) models. Achieving this goal, however, is met with challenges because of the varying densities of the point clouds and the complexity of the 3D data. [...] Read more.

The automating classification of point clouds capturing urban scenes is critical for supporting applications that demand three-dimensional (3D) models. Achieving this goal, however, is met with challenges because of the varying densities of the point clouds and the complexity of the 3D data. In order to increase the level of automation in the point cloud classification, this study proposes a segment-based parameter learning method that incorporates a two-dimensional (2D) land cover map, in which a strategy of fusing the 2D land cover map and the 3D points is first adopted to create labelled samples, and a formalized procedure is then implemented to automatically learn the following parameters of point cloud classification: the optimal scale of the neighborhood for segmentation, optimal feature set, and the training classifier. It comprises four main steps, namely: (1) point cloud segmentation; (2) sample selection; (3) optimal feature set selection; and (4) point cloud classification. Three datasets containing the point cloud data were used in this study to validate the efficiency of the proposed method. The first two datasets cover two areas of the National University of Singapore (NUS) campus while the third dataset is a widely used benchmark point cloud dataset of Oakland, Pennsylvania. The classification parameters were learned from the first dataset consisting of a terrestrial laser-scanning data and a 2D land cover map, and were subsequently used to classify both of the NUS datasets. The evaluation of the classification results showed overall accuracies of 94.07% and 91.13%, respectively, indicating that the transition of the knowledge learned from one dataset to another was satisfactory. The classification of the Oakland dataset achieved an overall accuracy of 97.08%, which further verified the transferability of the proposed approach. An experiment of the point-based classification was also conducted on the first dataset and the result was compared to that of the segment-based classification. The evaluation revealed that the overall accuracy of the segment-based classification is indeed higher than that of the point-based classification, demonstrating the advantage of the segment-based approaches. Full article

(This article belongs to the Special Issue 3D Modelling from Point Clouds: Algorithms and Methods)

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23 pages, 10007 KiB

Open AccessArticle

Towards Reconstructing 3D Buildings from ALS Data Based on Gestalt Laws

by Pingbo Hu, Bisheng Yang, Zhen Dong, Pengfei Yuan, Ronggang Huang, Hongchao Fan and Xuan Sun

Remote Sens. 2018, 10(7), 1127; https://doi.org/10.3390/rs10071127 - 17 Jul 2018

Cited by 29 | Viewed by 4940

Abstract

3D building models are an essential data infrastructure for various applications in a smart city system, since they facilitate spatial queries, spatial analysis, and interactive visualization. Due to the highly complex nature of building structures, automatically reconstructing 3D buildings from point clouds remains [...] Read more.

3D building models are an essential data infrastructure for various applications in a smart city system, since they facilitate spatial queries, spatial analysis, and interactive visualization. Due to the highly complex nature of building structures, automatically reconstructing 3D buildings from point clouds remains a challenging task. In this paper, a Roof Attribute Graph (RAG) method is proposed to describe the decomposition and topological relations within a complicated roof structure. Furthermore, top-down decomposition and bottom-up refinement processes are proposed to reconstruct roof parts according to the Gestalt laws, generating a complete structural model with a hierarchical topological tree. Two LiDAR datasets from Guangdong (China) and Vaihingen (Germany) with different point densities were used in our study. Experimental results, including the assessment on Vaihingen standardized by the International Society for Photogrammetry and Remote Sensing (ISPRS), show that the proposed method can be used to model 3D building roofs with high quality results as demonstrated by the completeness and correctness metrics presented in this paper. Full article

(This article belongs to the Special Issue 3D Modelling from Point Clouds: Algorithms and Methods)

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21 pages, 7576 KiB

Open AccessArticle

Feature Surface Extraction and Reconstruction from Industrial Components Using Multistep Segmentation and Optimization

by Yuan Wang, Jiajing Wang, Xiuwan Chen, Tianxing Chu, Maolin Liu and Ting Yang

Remote Sens. 2018, 10(7), 1073; https://doi.org/10.3390/rs10071073 - 5 Jul 2018

Cited by 15 | Viewed by 4692

Abstract

The structure of industrial components is diversified, and extensive efforts have been exerted to improve automation, accuracy, and completeness of feature surfaces extracted from such components. This paper presents a novel method called multistep segmentation and optimization for extracting feature surfaces from industrial [...] Read more.

The structure of industrial components is diversified, and extensive efforts have been exerted to improve automation, accuracy, and completeness of feature surfaces extracted from such components. This paper presents a novel method called multistep segmentation and optimization for extracting feature surfaces from industrial components. The method analyzes the normal vector distribution matrix to segment feature points from a 3D point cloud. The point cloud is then divided into different patches by applying the region growing method on the basis of the distance constraint and according to the initial results. Subsequently, each patch is fitted with an implicit expression equation, and the proposed method is combined with the random sample consensus (RANSAC) algorithm and parameter fitting to extract and optimize the feature surface. The proposed method is experimentally validated on three industrial components. The threshold setting in the algorithm is discussed in terms of algorithm principles and model features. Comparisons with state-of-the-art methods indicate that the proposed method for feature surface extraction is feasible and capable of achieving favorable performance and facilitating automation of industrial components. Full article

(This article belongs to the Special Issue 3D Modelling from Point Clouds: Algorithms and Methods)

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22 pages, 3247 KiB

Open AccessArticle

Model Selection for Parametric Surfaces Approximating 3D Point Clouds for Deformation Analysis

by Xin Zhao, Boris Kargoll, Mohammad Omidalizarandi, Xiangyang Xu and Hamza Alkhatib

Remote Sens. 2018, 10(4), 634; https://doi.org/10.3390/rs10040634 - 19 Apr 2018

Cited by 25 | Viewed by 5709

Abstract

Deformation monitoring of structures is a common application and one of the major tasks of engineering surveying. Terrestrial laser scanning (TLS) has become a popular method for detecting deformations due to high precision and spatial resolution in capturing a number of three-dimensional point [...] Read more.

Deformation monitoring of structures is a common application and one of the major tasks of engineering surveying. Terrestrial laser scanning (TLS) has become a popular method for detecting deformations due to high precision and spatial resolution in capturing a number of three-dimensional point clouds. Surface-based methodology plays a prominent role in rigorous deformation analysis. Consequently, it is of great importance to select an appropriate regression model that reflects the geometrical features of each state or epoch. This paper aims at providing the practitioner some guidance in this regard. Different from standard model selection procedures for surface models based on information criteria, we adopted the hypothesis tests from D.R. Cox and Q.H. Vuong to discriminate statistically between parametric models. The methodology was instantiated in two numerical examples by discriminating between widely used polynomial and B-spline surfaces as models of given TLS point clouds. According to the test decisions, the B-spline surface model showed a slight advantage when both surface types had few parameters in the first example, while it performed significantly better for larger numbers of parameters. Within B-spline surface models, the optimal one for the specific segment was fixed by Vuong’s test whose result was quite consistent with the judgment of widely used Bayesian information criterion. The numerical instabilities of B-spline models due to data gap were clearly reflected by the model selection tests, which rejected inadequate B-spline models in another numerical example. Full article

(This article belongs to the Special Issue 3D Modelling from Point Clouds: Algorithms and Methods)

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22 pages, 12811 KiB

Open AccessArticle

Unsupervised Clustering Method for Complexity Reduction of Terrestrial Lidar Data in Marshes

by Chuyen Nguyen, Michael J. Starek, Philippe Tissot and James Gibeaut

Remote Sens. 2018, 10(1), 133; https://doi.org/10.3390/rs10010133 - 18 Jan 2018

Cited by 18 | Viewed by 6308

Abstract

Accurate characterization of marsh elevation and landcover evolution is important for coastal management and conservation. This research proposes a novel unsupervised clustering method specifically developed for segmenting dense terrestrial laser scanning (TLS) data in coastal marsh environments. The framework implements unsupervised clustering with [...] Read more.

Accurate characterization of marsh elevation and landcover evolution is important for coastal management and conservation. This research proposes a novel unsupervised clustering method specifically developed for segmenting dense terrestrial laser scanning (TLS) data in coastal marsh environments. The framework implements unsupervised clustering with the well-known K-means algorithm by applying an optimization to determine the “k” clusters. The fundamental idea behind this novel framework is the application of multi-scale voxel representation of 3D space to create a set of features that characterizes the local complexity and geometry of the terrain. A combination of point- and voxel-generated features are utilized to segment 3D point clouds into homogenous groups in order to study surface changes and vegetation cover. Results suggest that the combination of point and voxel features represent the dataset well. The developed method compresses millions of 3D points representing the complex marsh environment into eight distinct clusters representing different landcover: tidal flat, mangrove, low marsh to high marsh, upland, and power lines. A quantitative assessment of the automated delineation of the tidal flat areas shows acceptable results considering the proposed method is unsupervised with no training data. Clustering results based on K-means are also compared to results based on the Self Organizing Map (SOM) clustering algorithm. Results demonstrate that the developed multi-scale voxelization approach and representative feature set are transferrable to other clustering algorithms, thereby providing an unsupervised framework for intelligent scene segmentation of TLS point cloud data in marshes. Full article

(This article belongs to the Special Issue 3D Modelling from Point Clouds: Algorithms and Methods)

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