Clustering based planar roof extraction from lidar data
01 Jan 2006-
TL;DR: An approach to generate 3-D models of buildings from lidar data collected from an urban setting by using an iterative combination of k-means and density based clustering methods.
Abstract: An approach to generate 3-D models of buildings from lidar data collected from an urban setting is presented. The present research focuses on extracting roof structures from a point cloud of a building using a combination of datamining techniques. To extract the roof structure, an assumption of planarity has been made, i.e. it is assumed that the roof can be modeled by a set of planar segments. The task then is to map each of the building point to a planar segment. We present a method that first separates points lying on or near breaklines, by which we mean points that are near the intersection of two planar surfaces or points that are near step-edges. Treating these points to be ambiguous, (i.e. they belong to more than one plane), we separate them from points that are exclusively planar. We then use neighborhood functions to determine what we call planar patches, and their direction cosines using their eigenvalue and eigenvector characteristics. Then an unsupervised data clustering technique to cluster these planar patches into one single plane is described. Most clustering techniques require that the number of clusters be known. In our case these clusters represent roof planes. We present a way to determine the number of roof planes (clusters) by using an iterative combination of k-means and density based clustering methods.
Citations
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TL;DR: An extended boundary regularization approach is developed based on multiple parallel and perpendicular line pairs to achieve topologically consistent and geometrically correct building models.
Abstract: This paper presents a solution framework for the segmentation and reconstruction of polyhedral building roofs from aerial LIght Detection And Ranging (lidar) point clouds. The eigenanalysis is first carried out for each roof point of a building within its Voronoi neighborhood. Such analysis not only yields the surface normal for each lidar point but also separates the lidar points into planar and nonplanar ones. In the second step, the surface normals of all planar points are clustered with the fuzzy k-means method. To optimize this clustering process, a potential-based approach is used to estimate the number of clusters, while considering both geometry and topology for the cluster similarity. The final step of segmentation separates the parallel and coplanar segments based on their distances and connectivity, respectively. Building reconstruction starts with forming an adjacency matrix that represents the connectivity of the segmented planar segments. A roof interior vertex is determined by intersecting all planar segments that meet at one point, whereas constraints in the form of vertical walls or boundary are applied to determine the vertices on the building outline. Finally, an extended boundary regularization approach is developed based on multiple parallel and perpendicular line pairs to achieve topologically consistent and geometrically correct building models. This paper describes the detail principles and implementation steps for the aforementioned solution framework. Results of a number of buildings with diverse roof complexities are presented and evaluated.
364 citations
Cites background from "Clustering based planar roof extrac..."
...In [25] and [26], the data set is divided into patches, each of which is evaluated with respect to its planarity....
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TL;DR: In this article, the authors summarized available data sets and relevant studies on recent developments in point cloud semantic segmentation and point cloud segmentation (PCS) for 3D point clouds.
Abstract: Ripe with possibilities offered by deep-learning techniques and useful in applications related to remote sensing, computer vision, and robotics, 3D point cloud semantic segmentation (PCSS) and point cloud segmentation (PCS) are attracting increasing interest. This article summarizes available data sets and relevant studies on recent developments in PCSS and PCS.
205 citations
TL;DR: In this paper, the authors provide a needed up-to-date review of recent developments in 3D Point Cloud Semantic Segmentation (PCSS) and discuss important issues and open questions.
Abstract: 3D Point Cloud Semantic Segmentation (PCSS) is attracting increasing interest, due to its applicability in remote sensing, computer vision and robotics, and due to the new possibilities offered by deep learning techniques. In order to provide a needed up-to-date review of recent developments in PCSS, this article summarizes existing studies on this topic. Firstly, we outline the acquisition and evolution of the 3D point cloud from the perspective of remote sensing and computer vision, as well as the published benchmarks for PCSS studies. Then, traditional and advanced techniques used for Point Cloud Segmentation (PCS) and PCSS are reviewed and compared. Finally, important issues and open questions in PCSS studies are discussed.
140 citations
TL;DR: Comparative studies with state-of-the-art methods demonstrate that the AGPN method obtains a promising, reliable, and high performance in detecting edges and tracing feature lines in 3D-point clouds.
Abstract: This paper presents an automated and effective method for detecting 3D edges and tracing feature lines from 3D-point clouds. This method is named Analysis of Geometric Properties of Neighborhoods (AGPN), and it includes two main steps: edge detection and feature line tracing. In the edge detection step, AGPN analyzes geometric properties of each query point’s neighborhood, and then combines RANdom SAmple Consensus (RANSAC) and angular gap metric to detect edges. In the feature line tracing step, feature lines are traced by a hybrid method based on region growing and model fitting in the detected edges. Our approach is experimentally validated on complex man-made objects and large-scale urban scenes with millions of points. Comparative studies with state-of-the-art methods demonstrate that our method obtains a promising, reliable, and high performance in detecting edges and tracing feature lines in 3D-point clouds. Moreover, AGPN is insensitive to the point density of the input data.
103 citations
Cites background from "Clustering based planar roof extrac..."
...The above-noted approaches can detect edges on regular feature lines such as plane intersection lines [13] or breaklines [15,16], roof or wall outlines [15,17] in buildings or piecewise planar objects....
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01 Jan 2009
TL;DR: A prototype algorithm supported by fast nearest neighborhood search and based on advanced similarity measures is proposed and implemented to segment point clouds directly and is efficient and robust comparing with algorithms based on image and TIN.
Abstract: The objective of segmentation on point clouds is to spatially group points with similar properties into homogeneous regions. Segmentation is a fundamental issue in processing point clouds data acquired by LiDAR and the quality of segmentation largely determines the success of information retrieval. Unlike the image or TIN model, the point clouds do not explicitly represent topology information. As a result, most existing segmentation methods for image and TIN have encountered two difficulties. First, converting data from irregular 3-D point clouds to other models usually leads to information loss; this is particularly a serious drawback for range image based algorithms. Second, the high computation cost of converting a large volume of point data is a considerable problem for any large scale LiDAR application. In this paper, we investigate the strategy to develop LiDAR segmentation methods directly based on point clouds data model. We first discuss several potential local similarity measures based on discrete computation geometry and machine learning. A prototype algorithm supported by fast nearest neighborhood search and based on advanced similarity measures is proposed and implemented to segment point clouds directly. Our experiments show that the proposed method is efficient and robust comparing with algorithms based on image and TIN. The paper will review popular segmentation methods in related disciplines and present the segmentation results of diverse buildings with different levels of difficulty.
66 citations
References
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TL;DR: An overview of pattern clustering methods from a statistical pattern recognition perspective is presented, with a goal of providing useful advice and references to fundamental concepts accessible to the broad community of clustering practitioners.
Abstract: Clustering is the unsupervised classification of patterns (observations, data items, or feature vectors) into groups (clusters). The clustering problem has been addressed in many contexts and by researchers in many disciplines; this reflects its broad appeal and usefulness as one of the steps in exploratory data analysis. However, clustering is a difficult problem combinatorially, and differences in assumptions and contexts in different communities has made the transfer of useful generic concepts and methodologies slow to occur. This paper presents an overview of pattern clustering methods from a statistical pattern recognition perspective, with a goal of providing useful advice and references to fundamental concepts accessible to the broad community of clustering practitioners. We present a taxonomy of clustering techniques, and identify cross-cutting themes and recent advances. We also describe some important applications of clustering algorithms such as image segmentation, object recognition, and information retrieval.
14,054 citations
TL;DR: In this paper, the authors proposed a method called the "gap statistic" for estimating the number of clusters (groups) in a set of data, which uses the output of any clustering algorithm (e.g. K-means or hierarchical), comparing the change in within-cluster dispersion with that expected under an appropriate reference null distribution.
Abstract: We propose a method (the ‘gap statistic’) for estimating the number of clusters (groups) in a set of data. The technique uses the output of any clustering algorithm (e.g. K-means or hierarchical), comparing the change in within-cluster dispersion with that expected under an appropriate reference null distribution. Some theory is developed for the proposal and a simulation study shows that the gap statistic usually outperforms other methods that have been proposed in the literature.
4,283 citations
Book•
01 Jan 2000
TL;DR: The gap statistic is proposed for estimating the number of clusters (groups) in a set of data by comparing the change in within‐cluster dispersion with that expected under an appropriate reference null distribution.
Abstract: We propose a method (the ‘gap statistic’) for estimating the number of clusters (groups) in a set of data. The technique uses the output of any clustering algorithm (e.g. K-means or hierarchical), comparing the change in within-cluster dispersion with that expected under an appropriate reference null distribution. Some theory is developed for the proposal and a simulation study shows that the gap statistic usually outperforms other methods that have been proposed in the literature.
3,860 citations
"Clustering based planar roof extrac..." refers background in this paper
...A further understanding of such a phenomenon can be obtained in Tibshirani et al (2001)....
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01 Jan 2006
TL;DR: This survey concentrates on clustering algorithms from a data mining perspective as a data modeling technique that provides for concise summaries of the data.
Abstract: Clustering is the division of data into groups of similar objects. In clustering, some details are disregarded in exchange for data simplification. Clustering can be viewed as a data modeling technique that provides for concise summaries of the data. Clustering is therefore related to many disciplines and plays an important role in a broad range of applications. The applications of clustering usually deal with large datasets and data with many attributes. Exploration of such data is a subject of data mining. This survey concentrates on clustering algorithms from a data mining perspective.
3,047 citations
"Clustering based planar roof extrac..." refers background in this paper
...Clustering algorithms can be broadly classified into hierarchical and partitioning algorithms (Jain et al 1999; Berkhin, 2002)....
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TL;DR: An efficient method for estimating cluster centers of numerical data that can be used to determine the number of clusters and their initial values for initializing iterative optimization-based clustering algorithms such as fuzzy C-means is presented.
Abstract: We present an efficient method for estimating cluster centers of numerical data. This method can be used to determine the number of clusters and their initial values for initializing iterative optimization-based clustering algorithms such as fuzzy C-means. Here we use the cluster estimation method as the basis of a fast and robust algorithm for identifying fuzzy models. A benchmark problem involving the prediction of a chaotic time series shows this model identification method compares favorably with other, more computationally intensive methods. We also illustrate an application of this method in modeling the relationship between automobile trips and demographic factors.
2,815 citations
"Clustering based planar roof extrac..." refers background in this paper
...Chiu (1994) further developed this idea by doing away with grids and using actual data point as cluster centers....
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