Massive Point Clouds Research Articles

A Hybrid Parallel Spatial Interpolation Algorithm for Massive LiDAR Point Clouds on Heterogeneous CPU-GPU Systems

Nowadays, heterogeneous CPU-GPU systems have become ubiquitous, but current parallel spatial interpolation (SI) algorithms exploit only one type of processing unit, and thus result in a waste of parallel resources. To address this problem, a hybrid parallel SI algorithm based on a thin plate spline is proposed to integrate both the CPU and GPU to further accelerate the processing of massive LiDAR point clouds. A simple yet powerful parallel framework is designed to enable simultaneous CPU-GPU interpolation, and a fast online training method is then presented to estimate the optimal decomposition granularity so that both types of processing units can run at maximum speed. Based on the optimal granularity, massive point clouds are continuously partitioned into a collection of discrete blocks in a data processing flow. A heterogeneous dynamic scheduler based on the greedy policy is also proposed to achieve better workload balancing. Experimental results demonstrate that the computing power of the CPU and GPU is fully utilized under conditions of optimal granularity, and the hybrid parallel SI algorithm achieves a significant performance boost when compared with the CPU-only and GPU-only algorithms. For example, the hybrid algorithm achieved a speedup of 20.2 on one of the experimental point clouds, while the corresponding speedups of using a CPU or a GPU alone were 8.7 and 12.6, respectively. The interpolation time was reduced by about 12% when using the proposed scheduler, in comparison with other common scheduling strategies.

TLS-based profile model analysis of major composite structures with robust B-spline method

With the development of city constructions, tunnels are becoming important structures for underground transportation. Tunnels constitute layered composite structures with concrete, reinforcement, waterproof layers, etc. Deformation monitoring of this kind of wide-ranging composite structure is significant to assure their safety considering the development of their complexity. Terrestrial laser scanning (TLS) is one of the most accurate and fast measurement technologies for deformation analysis. It has been applied widely in survey fields with the advantages of non-contact and panoramic acquisition of information. In this situation, TLS instruments are being developed rapidly, which necessitates high requirements regarding software aspects, especially concerning high-accuracy model construction. Therefore, developing a reliable method for 3D modeling with complex and massive point clouds is urgent. In this paper, we propose an adaptive expectation maximization (EM) method based on the scaled t-distribution for B-spline estimation, where automation is achieved for the best approximation with the maximum probability density. The innovation of this paper lies in offering a robust, automatic and time-efficient solution to model practical tunnel structures with a complex point cloud.

A Microtopographic Feature Analysis-Based LiDAR Data Processing Approach for the Identification of Chu Tombs

Most of the cultural sites hidden under dense vegetation in the mountains of China have been destroyed. In this paper, we present a microtopographic feature analysis (MFA)-based Light Detection and Ranging (LiDAR) data processing approach and an archaeological pattern-oriented point cloud segmentation (APoPCS) algorithm that we developed for the classification of archaeological objects and terrain points and the detection of archaeological remains. The archaeological features and patterns are interpreted and extracted from LiDAR point cloud data to construct an archaeological object pattern database. A microtopographic factor is calculated based on the archaeological object patterns, and this factor converts the massive point cloud data into a raster feature image. A fuzzy clustering algorithm based on the archaeological object patterns is presented for raster feature image segmentation and the detection of archaeological remains. Using the proposed approach, we investigated four typical areas with different types of Chu tombs in Central China, which had dense vegetation and high population densities. Our research results show that the proposed LiDAR data processing approach can identify archaeological remains from large-volume and massive LiDAR data, as well as in areas with dense vegetation and trees. The studies of different archaeological object patterns are important for improving the robustness of the proposed APoPCS algorithm for the extraction of archaeological remains.

INTERACTIVE CLASSIFICATION OF CONSTRUCTION MATERIALS: FEEDBACK DRIVEN FRAMEWORK FOR ANNOTATION AND ANALYSIS OF 3D POINT CLOUDS

Abstract. Digital documentation of cultural heritage structures is increasingly more common through the application of different imaging techniques. Many works have focused on the application of laser scanning and photogrammetry techniques for the acquisition of threedimensional (3D) geometry detailing cultural heritage sites and structures. With an abundance of these 3D data assets, there must be a digital environment where these data can be visualized and analyzed. Presented here is a feedback driven visualization framework that seamlessly enables interactive exploration and manipulation of massive point cloud data. The focus of this work is on the classification of different building materials with the goal of building more accurate as-built information models of historical structures. User defined functions have been tested within the interactive point cloud visualization framework to evaluate automated and semi-automated classification of 3D point data. These functions include decisions based on observed color, laser intensity, normal vector or local surface geometry. Multiple case studies are presented here to demonstrate the flexibility and utility of the presented point cloud visualization framework to achieve classification objectives.

Automatic Detection and Classification of Pole-Like Objects for Urban Cartography Using Mobile Laser Scanning Data.

Mobile laser scanning (MLS) is a modern and powerful technology capable of obtaining massive point clouds of objects in a short period of time. Although this technology is nowadays being widely applied in urban cartography and 3D city modelling, it has some drawbacks that need to be avoided in order to strengthen it. One of the most important shortcomings of MLS data is concerned with the fact that it provides an unstructured dataset whose processing is very time-consuming. Consequently, there is a growing interest in developing algorithms for the automatic extraction of useful information from MLS point clouds. This work is focused on establishing a methodology and developing an algorithm to detect pole-like objects and classify them into several categories using MLS datasets. The developed procedure starts with the discretization of the point cloud by means of a voxelization, in order to simplify and reduce the processing time in the segmentation process. In turn, a heuristic segmentation algorithm was developed to detect pole-like objects in the MLS point cloud. Finally, two supervised classification algorithms, linear discriminant analysis and support vector machines, were used to distinguish between the different types of poles in the point cloud. The predictors are the principal component eigenvalues obtained from the Cartesian coordinates of the laser points, the range of the Z coordinate, and some shape-related indexes. The performance of the method was tested in an urban area with 123 poles of different categories. Very encouraging results were obtained, since the accuracy rate was over 90%.

3D building roof reconstruction from airborne LiDAR point clouds: a framework based on a spatial database

ABSTRACTThree-dimensional (3D) building models are essential for 3D Geographic Information Systems and play an important role in various urban management applications. Although several light detection and ranging (LiDAR) data-based reconstruction approaches have made significant advances toward the fully automatic generation of 3D building models, the process is still tedious and time-consuming, especially for massive point clouds. This paper introduces a new framework that utilizes a spatial database to achieve high performance via parallel computation for fully automatic 3D building roof reconstruction from airborne LiDAR data. The framework integrates data-driven and model-driven methods to produce building roof models of the primary structure with detailed features. The framework is composed of five major components: (1) a density-based clustering algorithm to segment individual buildings, (2) an improved boundary-tracing algorithm, (3) a hybrid method for segmenting planar patches that selects seed points in parameter space and grows the regions in spatial space, (4) a boundary regularization approach that considers outliers and (5) a method for reconstructing the topological and geometrical information of building roofs using the intersections of planar patches. The entire process is based on a spatial database, which has the following advantages: (a) managing and querying data efficiently, especially for millions of LiDAR points, (b) utilizing the spatial analysis functions provided by the system, reducing tedious and time-consuming computation, and (c) using parallel computing while reconstructing 3D building roof models, improving performance.

Anti-aliasing filters for deriving high-accuracy DEMs from TLS data: A case study from Freeport, Texas

Terrestrial laser scanning (TLS), also known as ground-based Light Detection and Ranging (LiDAR), has been frequently applied to build bare-earth digital elevation models (DEMs) for high-accuracy geomorphology studies. The point clouds acquired from TLS often achieve a spatial resolution at fingerprint (e.g., 3cm×3cm) to handprint (e.g., 10cm×10cm) level. A downsampling process has to be applied to decimate the massive point clouds and obtain manageable DEMs. It is well known that downsampling can result in aliasing that causes different signal components to become indistinguishable when the signal is reconstructed from the datasets with a lower sampling rate. Conventional DEMs are mainly the results of upsampling of sparse elevation measurements from land surveying, satellite remote sensing, and aerial photography. As a consequence, the effects of aliasing caused by downsampling have not been fully investigated in the open literature of DEMs. This study aims to investigate the spatial aliasing problem of regridding dense TLS data. The TLS data collected from the beach and dune area near Freeport, Texas in the summer of 2015 are used for this study. The core idea of the anti-aliasing procedure is to apply a low-pass spatial filter prior to conducting downsampling. This article describes the successful use of a fourth-order Butterworth low-pass spatial filter employed in the Generic Mapping Tools (GMT) software package as an anti-aliasing filter. The filter can be applied as an isotropic filter with a single cutoff wavelength or as an anisotropic filter with two different cutoff wavelengths in the X and Y directions. The cutoff wavelength for the isotropic filter is recommended to be three times the grid size of the target DEM.

An Approach of Web-based Point Cloud Visualization without Plug-in

With the advances in three-dimensional laser scanning technology, the demand for visualization of massive point cloud is increasingly urgent, but a few years ago point cloud visualization was limited to desktop-based solutions until the introduction of WebGL, several web renderers are available. This paper addressed the current issues in web-based point cloud visualization, and proposed a method of web-based point cloud visualization without plug-in. The method combines ASP.NET and WebGL technologies, using the spatial database PostgreSQL to store data and the open web technologies HTML5 and CSS3 to implement the user interface, a visualization system online for 3D point cloud is developed by Javascript with the web interactions. Finally, the method is applied to the real case. Experiment proves that the new model is of great practical value which avoids the shortcoming of the existing WebGIS solutions.

Road Curb Extraction From Mobile LiDAR Point Clouds

Automatic extraction of road curbs from uneven, unorganized, noisy and massive 3D point clouds is a challenging task. Existing methods often project 3D point clouds onto 2D planes to extract curbs. However, the projection causes loss of 3D information which degrades the performance of the detection. This paper presents a robust, accurate and efficient method to extract road curbs from 3D mobile LiDAR point clouds. Our method consists of two steps: 1) extracting the candidate points of curbs based on the proposed novel energy function and 2) refining the candidate points using the proposed least cost path model. We evaluated our method on a large-scale of residential area (16.7GB, 300 million points) and an urban area (1.07GB, 20 million points) mobile LiDAR point clouds. Results indicate that the proposed method is superior to the state-of-the-art methods in terms of robustness, accuracy and efficiency. The proposed curb extraction method achieved a completeness of 78.62% and a correctness of 83.29%. These experiments demonstrate that the proposed method is a promising solution to extract road curbs from mobile LiDAR point clouds.

Error-aware construction and rendering of multi-scan panoramas from massive point clouds

Obtaining 3D realistic models of urban scenes from accurate range data is nowadays an important research topic, with applications in a variety of fields ranging from Cultural Heritage and digital 3D archiving to monitoring of public works. Processing massive point clouds acquired from laser scanners involves a number of challenges, from data management to noise removal, model compression and interactive visualization and inspection. In this paper, we present a new methodology for the reconstruction of 3D scenes from massive point clouds coming from range lidar sensors. Our proposal includes a panorama-based compact reconstruction where colors and normals are estimated robustly through an error-aware algorithm that takes into account the variance of expected errors in depth measurements. Our representation supports efficient, GPU-based visualization with advanced lighting effects. We discuss the proposed algorithms in a practical application on urban and historical preservation, described by a massive point cloud of 3.5 billion points. We show that we can achieve compression rates higher than 97% with good visual quality during interactive inspections.

The D-FCM partitioned D-BSP tree for massive point cloud data access and rendering

The spatial partitioning of massive point cloud data involves dividing the space into a multi-tree structure step by step, so as to achieve the purpose of fast access and to render the point cloud. The current methods are based on spatial regularity and equal division, which is not consistent with the irregular and non-uniform distribution of most point clouds. This paper presents a directional fuzzy c-means (D-FCM) method for irregular spatial partitioning. The distance metric is weighted by a direction coefficient, which is determined by the eigenvalue of the point cloud. The orientation of each node is adaptively calculated by principal component analysis of the point cloud, and Karhunen-Loeve (KL) transform is applied to the points coordinates in node. A binary space partitioning (BSP) tree structure is used to partition the point cloud data node by node, and a directional BSP (D-BSP) tree is formed. The D-BSP tree structure was tested with point clouds of 0.1 million to over 2 billion points (up to 60GB). The experimental results showed that the D-BSP tree can ensure that the bounding boxes are close to the actual spatial distribution of the point cloud, it can completely expand along the spatial configuration of the point cloud without generating unnecessary partitioning, and it can achieve a higher rendering speed with less memory requirement.

TerraNNI

With modern focus on remote sensing technology, such as LiDAR, the amount of spatial data, in the form of massive point clouds, has increased dramatically. Furthermore, repeated surveys of the same areas are becoming more common. This trend will only increase as topographic changes prompt surveys over already scanned areas, in which case we obtain large spatiotemporal datasets. An initial step in the analysis of such spatial data is to create a digital elevation model representing the terrain, possibly over time. In the case of spatial (spatiotemporal, respectively) datasets, these models often represent elevation on a 2D (3D, respectively) grid. This involves interpolating the elevation of LiDAR points on these grid points. In this article, we show how to efficiently perform natural neighbor interpolation over a 2D and 3D grid. Using a graphics processing unit (GPU), we describe different algorithms to attain speed and GPU-memory tradeoffs. Our experimental results demonstrate that our algorithms not only are significantly faster than earlier ones but also scale to much bigger datasets that previous algorithms were unable to handle.

FACETS : A CLOUDCOMPARE PLUGIN TO EXTRACT GEOLOGICAL PLANES FROM UNSTRUCTURED 3D POINT CLOUDS

Geological planar facets (stratification, fault, joint…) are key features to unravel the tectonic history of rock outcrop or appreciate the stability of a hazardous rock cliff. Measuring their spatial attitude (dip and strike) is generally performed by hand with a compass/clinometer, which is time consuming, requires some degree of censoring (i.e. refusing to measure some features judged unimportant at the time), is not always possible for fractures higher up on the outcrop and is somewhat hazardous. 3D virtual geological outcrop hold the potential to alleviate these issues. Efficiently segmenting massive 3D point clouds into individual planar facets, inside a convenient software environment was lacking. FACETS is a dedicated plugin within CloudCompare v2.6.2 (<a href="http://cloudcompare.org/"target="_blank">http://cloudcompare.org/</a> ) implemented to perform planar facet extraction, calculate their dip and dip direction (i.e. azimuth of steepest decent) and report the extracted data in interactive stereograms. Two algorithms perform the segmentation: Kd-Tree and Fast Marching. Both divide the point cloud into sub-cells, then compute elementary planar objects and aggregate them progressively according to a planeity threshold into polygons. The boundaries of the polygons are adjusted around segmented points with a tension parameter, and the facet polygons can be exported as 3D polygon shapefiles towards third party GIS software or simply as ASCII comma separated files. One of the great features of FACETS is the capability to explore planar objects but also 3D points with normals with the stereogram tool. Poles can be readily displayed, queried and manually segmented interactively. The plugin blends seamlessly into CloudCompare to leverage all its other 3D point cloud manipulation features. A demonstration of the tool is presented to illustrate these different features. While designed for geological applications, FACETS could be more widely applied to any planar objects.

FACETS : A CLOUDCOMPARE PLUGIN TO EXTRACT GEOLOGICAL PLANES FROM UNSTRUCTURED 3D POINT CLOUDS

Abstract. Geological planar facets (stratification, fault, joint…) are key features to unravel the tectonic history of rock outcrop or appreciate the stability of a hazardous rock cliff. Measuring their spatial attitude (dip and strike) is generally performed by hand with a compass/clinometer, which is time consuming, requires some degree of censoring (i.e. refusing to measure some features judged unimportant at the time), is not always possible for fractures higher up on the outcrop and is somewhat hazardous. 3D virtual geological outcrop hold the potential to alleviate these issues. Efficiently segmenting massive 3D point clouds into individual planar facets, inside a convenient software environment was lacking. FACETS is a dedicated plugin within CloudCompare v2.6.2 (http://cloudcompare.org/ ) implemented to perform planar facet extraction, calculate their dip and dip direction (i.e. azimuth of steepest decent) and report the extracted data in interactive stereograms. Two algorithms perform the segmentation: Kd-Tree and Fast Marching. Both divide the point cloud into sub-cells, then compute elementary planar objects and aggregate them progressively according to a planeity threshold into polygons. The boundaries of the polygons are adjusted around segmented points with a tension parameter, and the facet polygons can be exported as 3D polygon shapefiles towards third party GIS software or simply as ASCII comma separated files. One of the great features of FACETS is the capability to explore planar objects but also 3D points with normals with the stereogram tool. Poles can be readily displayed, queried and manually segmented interactively. The plugin blends seamlessly into CloudCompare to leverage all its other 3D point cloud manipulation features. A demonstration of the tool is presented to illustrate these different features. While designed for geological applications, FACETS could be more widely applied to any planar objects.

Fast and Accurate Plane Segmentation of Airborne LiDAR Point Cloud Using Cross-Line Elements

Plane segmentation is an important step in feature extraction and 3D modeling from light detection and ranging (LiDAR) point cloud. The accuracy and speed of plane segmentation are two issues difficult to balance, particularly when dealing with a massive point cloud with millions of points. A fast and easy-to-implement algorithm of plane segmentation based on cross-line element growth (CLEG) is proposed in this study. The point cloud is converted into grid data. The points are segmented into line segments with the Douglas-Peucker algorithm. Each point is then assigned to a cross-line element (CLE) obtained by segmenting the points in the cross-directions. A CLE determines one plane, and this is the rationale of the algorithm. CLE growth and point growth are combined after selecting the seed CLE to obtain the segmented facets. The CLEG algorithm is validated by comparing it with popular methods, such as RANSAC, 3D Hough transformation, principal component analysis (PCA), iterative PCA, and a state-of-the-art global optimization-based algorithm. Experiments indicate that the CLEG algorithm runs much faster than the other algorithms. The method can produce accurate segmentation at a speed of 6 s per 3 million points. The proposed method also exhibits good accuracy.

Fast exact shortest distance queries for massive point clouds

This paper describes a new efficient algorithm for the rapid computation of exact shortest distances between a point cloud and another object (e.g. triangulated, point-based, etc.) in three dimensions. It extends the work presented in Eriksson and Shellshear (2014) where only approximate distances were computed on a simplification of a massive point cloud. Here, the fast computation of the exact shortest distance is achieved by pruning large subsets of the point cloud known not to be closest to the other object. The approach works for massive point clouds even with a small amount of RAM and is able to provide real time performance. Given a standard PC with only 8GB of RAM, this resulted in real-time shortest distance computations of 15 frames per second for a point cloud having 1 billion points in three dimensions.

3D measuring and segmentation method for hot heavy forging

Non-contact, three-dimensional (3D) measurements of hot heavy forgings (HHFs) are desirable for permitting real-time process control, whereas most current methods are usually inconvenient or invalid due to the difficulty in dealing with the hot heavy forgings. This paper presents a new measuring and combined segmentation approach that employs a two-dimensional (2D) laser radar with additional rotation driven by the servomotor that scans the forgings to acquire a massive 3D point cloud dataset. From this dataset, the desired forging part is roughly distinguished from the background based on the angle and distance continuity constraints, and then refined by the curvature-based border extraction method and further segmented by the hierarchical clustering analysis method. Finally, the feature points are extracted based on the normal vector variation and fitted to convey the 3D information. This novel method has been verified by experiments both in the laboratory and the forging workshop for hot heavy forging pieces, with a dimension error of less than 2%. These results indicate that the proposed approach is more practical and convenient than current methods for real-time, on-site measurements of HHFs.

Structure-based object detection from scene point clouds

We present a novel algorithm for object detection from the scene point clouds acquired in complex environments based on the structure analysis of objects. First, the scene point clouds are partitioned using the Gaussian map, and the primitive shapes are extracted from the segments. Second, each primitive shape is represented by a node, and the connection between two shapes is represented by an edge. The topological graph of the scene is reconstructed by defining the node properties, edge properties, and connection types, and the structure of the target objects is also analyzed. Then, an “assembly-matching” strategy is proposed to recognize the target objects in the scene. The qualified primitive shapes are assembled iteratively until no more suitable shapes are found. At the same time, the connection string of the combinational shapes is recorded using several numbers. Finally, the target object is detected by comparison to the connection string. The object is detected successfully if the structure coding between the iterative shapes is in line with the target object. The experimental results show that the proposed method can quickly detect common objects from massive point clouds.

VISUALIZING HIGH RESOLUTION THREEDIMENSIONAL AND TWO-DIMENSIONAL DATA OF CULTURAL HERITAGE SITES

The combination of 3D acquisition (terrestrial and airborne LiDAR, structured light, structure-from-motion) and 2D imaging (photographic, multispectral, panoramic, orthorectified, reflectance transformation) techniques allows the geometry, appearance and other aspects of culturally significant sites to be objectively documented. Traditionally, these data are usually transformed into models such as 3D textured meshes before they are visualized or analyzed—an often time- and effort-intensive process. We propose a system for the direct visualization and analysis of such data, allowing the different aspects recorded to be layered together, and co-visualized with annotations and other relevant information. We describe the required technical foundations, including gigapoint and gigapixel visualization pipelines that enable the dynamic layering of high-resolution imagery over massive minimally-processed LiDAR point clouds that serve as the base spatial layer. In particular, we introduce the pointbuffer—a GPU-resident viewdependent point cache—as the foundation of our gigapoint pipeline, and outline the use of virtual texturing for draping of gigapixel imagery onto point clouds. Finally, we present case studies from sites in Jordan and Italy.

Interest Point Detection in 3D Point Cloud Data Using 3D Sobel-Harris Operator

Manual selection of features from massive unstructured point cloud data is a very time-consuming task that requires a considerable amount of human intervention. This work is motivated by the need of fast and simple algorithm to obtain robust, stable and well-localized interest points that are used for subsequent processing in computer vision real-time applications. This paper presents an algorithm for detection of interest points in three-dimensional (3D) point cloud data by using a combined 3D Sobel–Harris operator. The proposed algorithm is compared with six state-of-the-art approaches used to identify the true feature points. Extensive experiments were carried out using synthetic benchmark and real datasets. The datasets were selected with different sizes, features and scales. The results were evaluated against human generated ground truth and predefined feature points. Three measures were used to evaluate the algorithm accuracy, namely localization accuracy Le, False Positive Error (FPE) and False Negative Errors (FNE). Also, the complexity analysis of the proposed algorithm is presented. The results show that the proposed algorithm can identify the interest points with accepted accuracy. It works directly on point cloud datasets and shows superiority when compared with other methods work on 3D mesh data.