Three-dimensional Point Cloud Research Articles

Application of 3D laser scanning technology for preservation and monitoring of Thai pagoda: A case study of Wat Krachee Ayutthaya

3D laser scanning technology is one of the current technologies for developing a sustainable and smart city. This technology is applied to obtain the three-dimensional information of building in digital format. To preserve the historic structure, the current structural information is required for structural assessment, monitoring, and conservation planning. The data obtained by the 3D laser scanner is normally used to generate the three-dimensional point cloud data of the building surface. The high-density point cloud data represented the dimension, shape, and size of the building in the current state can be acquired in more accurate and faster than another technique. This paper aims to present the application of 3D laser scanning technology for preservation and monitoring of heritage pagoda in Thailand. The pagoda of Wat Krachee located in Ayutthaya province of Thailand was selected as a case study. The results obtained in this study are a part of Wat Krachee conservation project, which is led by UNESCO Bangkok and the Fine Arts Department, Thailand. One of the challenges in Wat Krachee project is how to preserve the masonry pagoda with two intertwined trees growing inside and become a part of pagoda structure. The terrestrial 3D laser scanner was applied to develop the digital data documentation and 3D model of pagoda in the current state. The 3D survey work has been started since February 2018. The analysis of data obtained in February 2018, Mar 2018, July 2018, and July 2019 was carried out. The significant dimensions and deformation of pagoda are monitored. According to the results, it is found that the 3D laser scanning technology is very effective for assessment and monitoring. The 3D point cloud model obtained in this study can be used to develop the assume original model of the Wat Krachee pagoda, which is the inverted bell-shape with a square base.

Extraction and statistics of discontinuity orientation and trace length from typical fractured rock mass: A case study of the Xinchang underground research laboratory site, China

The geological parameters of rock discontinuities are of great significance for the evaluation of high-level radioactive waste repository and underground research laboratory (URL) site characteristics. To provide sufficient basis for the construction of the Xinchang URL site, this paper presents a comprehensive set of methods for acquisition of discontinuity parameters. In this set of methods, a high precision three-dimensional point cloud model of an outcrop with real geographic information is established based on close-range photogrammetry technology, from which not only planar discontinuities but also linear discontinuities can be identified and extracted by a semi-automatic method. A new fast-fuzzy clustering method is proposed to cluster discontinuity sets. The statistics method based on topographic window projection is improved to increase the rationality of estimating the geometric characteristics of fracture traces. As a preliminary validation, the results of this case study reveal that these methods have good applicability in acquiring discontinuity parameters, which is also conducive to the establishment of a deterministic-stochastic discrete fracture network (DSDFN) model to further analyze the properties of fractured rock mass. In addition, it is preliminarily found that some outcrops different locations have structures with similar geological features to a certain extent; moreover, the fractures exposed on the outcrop surface have distribution characteristics similar to those in the granite body obtained by drilling. Above all, the presented multidisciplinary methods could provide an alternative and practical approach for low-cost and accurate acquisition of discontinuity parameters, and thus, to study the properties of fractured rock mass effectively.

Implementation of ultra-light UAV systems for cultural heritage documentation

Abstract New technologies, such as unmanned aerial vehicles (UAVs) and terrestrial laser scanning systems, provide opportunities for the digital documentation of cultural heritage. Although ultra-light drones (ULDs) are usually used for recreational activities, in this study, we examined their ability for use in digital documentation. We investigated the efficiency of an ULD for documentation of a selected historical building. In this study, the structure from motion (SfM) method was used to create three-dimensional point cloud data of a historical building using ULD and a low-cost UAV. The resulting point clouds were compared with terrestrial laser scanner data. The maximum standard deviations were calculated as 0.62 cm and 1.87 cm for low-cost UAV and ULD, respectively. The results show that ULDs can be used under suitable circumstances as a low-cost alternative for cultural heritage documentation.

Classification using a three-dimensional sensor in a structured industrial environment

Usage of a three-dimensional (3-D) sensor and point clouds provides various benefits over the usage of a traditional camera for industrial inspection. We focus on the development of a classification solution for industrial inspection purposes using point clouds as an input. The developed approach employs deep learning to classify point clouds, acquired via a 3-D sensor, the final goal being to verify the presence of certain industrial elements in the scene. We possess the computer-aided design model of the whole mechanical assembly and an in-house developed localization module provides initial pose estimation from which 3-D point clouds of the elements are inferred. The accuracy of this approach is proved to be acceptable for industrial usage. Robustness of the classification module in relation to the accuracy of the localization algorithm is also estimated.

Comparison of terrestrial lidar, SfM, and MBES resolution and accuracy for geomorphic analyses in physical systems that experience subaerial and subaqueous conditions

This study provides a systematic comparison of lidar, Multibeam Echo Sounders (MBES), and Structure-from-Motion (SfM) derived datasets collected over a fixed surface contained within a large outdoor flume converted to a 15 × 2.5 × 1.5 m tank. The surface incorporated various morphological features and structures for measurement technique comparison over varying surface type and geometries. Two lidar scans and two photograph sets (used in SfM) capture the subaerial surface within the experimental tank before and after the tank was filled for MBES measurements. MBES surveys at varied frequencies (200 and 400 kHz), swath widths (90° and 150°), and phase/amplitude blends (0 and 84%) settings were conducted along longitudinal transects. Post-processing of the data delivers three-dimensional point clouds that are georeferenced via RTK-GNSS measurements of control points within the experimental tank. All datasets are compared to the first lidar dataset (lidar1), herein defined as the reference surface. Calculated root mean square error (RMSE) for each dataset results in 0.3–3.37 cm error among the three systems under the experimental conditions. Datasets are then detrended to determine how each instrument detects individual geomorphic features at various resolutions. The wide swath width of the MBES generated the largest difference between surfaces, which may be attributed to echoing from the cement tank walls. Error found among techniques suggest that caution should be used when resolving textures < 3.5 cm with MBES, but is dependent on distance and point spacing during the survey.

Supervised Learning of Natural-Terrain Traversability with Synthetic 3D Laser Scans

Autonomous navigation of ground vehicles on natural environments requires looking for traversable terrain continuously. This paper develops traversability classifiers for the three-dimensional (3D) point clouds acquired by the mobile robot Andabata on non-slippery solid ground. To this end, different supervised learning techniques from the Python library Scikit-learn are employed. Training and validation are performed with synthetic 3D laser scans that were labelled point by point automatically with the robotic simulator Gazebo. Good prediction results are obtained for most of the developed classifiers, which have also been tested successfully on real 3D laser scans acquired by Andabata in motion.

Development and validation of statistical shape models of the primary functional bone segments of the foot.

IntroductionMusculoskeletal models are important tools for studying movement patterns, tissue loading, and neuromechanics. Personalising bone anatomy within models improves analysis accuracy. Few studies have focused on personalising foot bone anatomy, potentially incorrectly estimating the foot’s contribution to locomotion. Statistical shape models have been created for a subset of foot-ankle bones, but have not been validated. This study aimed to develop and validate statistical shape models of the functional segments in the foot: first metatarsal, midfoot (second-to-fifth metatarsals, cuneiforms, cuboid, and navicular), calcaneus, and talus; then, to assess reconstruction accuracy of these shape models using sparse anatomical data.MethodsMagnetic resonance images of 24 individuals feet (age = 28 ± 6 years, 52% female, height = 1.73 ± 0.8 m, mass = 66.6 ± 13.8 kg) were manually segmented to generate three-dimensional point clouds. Point clouds were registered and analysed using principal component analysis. For each bone segment, a statistical shape model and principal components were created, describing population shape variation. Statistical shape models were validated by assessing reconstruction accuracy in a leave-one-out cross validation. Statistical shape models were created by excluding a participant’s bone segment and used to reconstruct that same excluded bone using full segmentations and sparse anatomical data (i.e. three discrete points on each segment), for all combinations in the dataset. Tali were not reconstructed using sparse anatomical data due to a lack of externally accessible landmarks. Reconstruction accuracy was assessed using Jaccard index, root mean square error (mm), and Hausdorff distance (mm).ResultsReconstructions generated using full segmentations had mean Jaccard indices between 0.77 ± 0.04 and 0.89 ± 0.02, mean root mean square errors between 0.88 ± 0.19 and 1.17 ± 0.18 mm, and mean Hausdorff distances between 2.99 ± 0.98 mm and 6.63 ± 3.68 mm. Reconstructions generated using sparse anatomical data had mean Jaccard indices between 0.67 ± 0.06 and 0.83 ± 0.05, mean root mean square error between 1.21 ± 0.54 mm and 1.66 ± 0.41 mm, and mean Hausdorff distances between 3.21 ± 0.94 mm and 7.19 ± 3.54 mm. Jaccard index was higher (P < 0.01) and root mean square error was lower (P < 0.01) in reconstructions from full segmentations compared to sparse anatomical data. Hausdorff distance was lower (P < 0.01) for midfoot and calcaneus reconstructions using full segmentations compared to sparse anatomical data.ConclusionFor the first time, statistical shape models of the primary functional segments of the foot were developed and validated. Foot segments can be reconstructed with minimal error using full segmentations and sparse anatomical landmarks. In future, larger training datasets could increase statistical shape model robustness, extending use to paediatric or pathological populations.

Merge-Swap Optimization Framework for Supervoxel Generation from Three-Dimensional Point Clouds

Surpervoxels are becoming increasingly popular in many point cloud processing applications. However, few methods have been devised specifically for generating compact supervoxels from unstructured three-dimensional (3D) point clouds. In this study, we aimed to generate high quality over-segmentation of point clouds. We propose a merge-swap optimization framework that solves any supervoxel generation problem formulated in energy minimization. In particular, we tailored an energy function that explicitly encourages regular and compact supervoxels with adaptive size control considering local geometric information of point clouds. We also provide two acceleration techniques to reduce the computational overhead. The performance of the proposed merge-swap optimization approach is superior to that of previous work in terms of thorough optimization, computational efficiency, and practical applicability to incorporating control of other properties of supervoxels. The experiments show that our approach produces supervoxels with better segmentation quality than two state-of-the-art methods on three public datasets.

Utilizing extended geocodes for handling massive three-dimensional point cloud data

Point clouds have become a primitive and fundamental material for manifold spatial representations. It can precisely render real-world environments as high-density points which include three-dimensional (3D) coordinates (x, y & z) and other features (color, intensity, and so on). Accordingly, various applications, including robot navigation and self-driving, make use of point clouds not only to detect near objects but to comprehend overall geospatial surroundings. However, it is challenging to exploit the point clouds in terms of spatial query processing in traditional database systems because of its enormous volume and nonstructural formats. In this paper, we propose an efficient method for the manipulation of 3D point cloud based on a Discrete Global Grid System (DGGS). As DGGS represents the Earth as hierarchical sequences of equal area/volume tessellations, it provides an accurate partitioning to integrate and analyze big geospatial data, unlike a base64 geohash representation. This study extends our previous DGGS-based encoding/decoding work to process 3D range queries with more than 64 bits for precise 3D coordinates of point clouds. In particular, we apply PH-tree as a multi-resolution tessellation storage and indexing structure for 3D bounding box queries. The experimental results show that our query processing significantly outperforms the baseline with a linear quadtree. Also, we present the encoding/decoding efficiency of converting large Morton codes from geographic coordinates by using the combination of bit interleaving and lookup tables.

Towards Spatially Explicit Quantification of Pre- and Postfire Fuels and Fuel Consumption from Traditional and Point Cloud Measurements

Abstract Methods to accurately estimate spatially explicit fuel consumption are needed because consumption relates directly to fire behavior, effects, and smoke emissions. Our objective was to quantify sparkleberry (Vaccinium arboretum Marshall) shrub fuels before and after six experimental prescribed fires at Fort Jackson in South Carolina. We used a novel approach to characterize shrubs non-destructively from three-dimensional (3D) point cloud data collected with a terrestrial laser scanner. The point cloud data were reduced to 0.001 m–3 voxels that were either occupied to indicate fuel presence or empty to indicate fuel absence. The density of occupied voxels was related significantly by a logarithmic function to 3D fuel bulk density samples that were destructively harvested (adjusted R2 = .32, P &amp;lt; .0001). Based on our findings, a survey-grade Global Navigation Satellite System may be necessary to accurately associate 3D point cloud data to 3D fuel bulk density measurements destructively collected in small (submeter) shrub plots. A recommendation for future research is to accurately geolocate and quantify the occupied volume of entire shrubs as 3D objects that can be used to train models to map shrub fuel bulk density from point cloud data binned to occupied 3D voxels.

An edge-sensitive simplification method for scanned point clouds

Due to the huge number of points on three-dimensional point clouds captured by optical scanning devices, point-based simplification is a crucial step in model reconstruction. However, the loss of edge features of industrial parts after such simplification reduces reconstruction accuracy. This paper presents an edge-sensitive, point-based simplification method to eliminate redundant points and preserve more edge feature details. Firstly, a new geometrical descriptor is created for each point to generate a geometrical domain. A clustering scheme is then designed by applying two different clustering algorithms, to split the point cloud in the geometrical domain and the spatial domain respectively. The proposed method is capable of preserving edge features well, while reducing the original number of points to 10% or even 5%. The proposed method is compared with other simplification methods and the experimental results indicate that it performs better in simplifying industrial parts.

Relative pose determination of uncooperative known target based on extracting region of interest

This paper deals with the problem of relative pose determination between a model-known uncooperative space target and a chaser spacecraft using three-dimensional point cloud. A novel, reliable and real-time relative pose determination framework is proposed, which is composed of a region of interest–based initial pose acquisition method and a template matching iterative closest point algorithm for tracking pose. The initial pose is obtained by the principal component analysis method which aligns the region of interest extracted from the scanning point cloud with the region of interest of the known model point cloud, and a three-dimensional convex hull is constructed to extract the region of interest fast. To improve the iterative closest point, the registration between the template point cloud generated by the pose of last frame and the point cloud of the current frame is used to replace the registration between the local scanning point cloud and the global model point cloud. In addition, the performance (stability, low cost and their robustness against noise) of the proposed initial pose acquisition method and the accuracy and reliability of the pose tracking algorithm have been demonstrated by the numerical experiments. Finally, the effectiveness of the proposed method is verified by the field experiment.

Hypergraph Spectral Clustering for Point Cloud Segmentation

Hypergraph spectral analysis has emerged as an effective tool processing complex data structures in data analysis. The surface of a three-dimensional (3D) point cloud, and the multilateral relationship among their points can be naturally captured by the high-dimensional hyperedges. This work investigates the power of hypergraph spectral analysis in unsupervised segmentation of 3D point clouds. We estimate, and order the hypergraph spectrum from observed point cloud coordinates. By trimming the redundancy from the estimated hypergraph spectral space based on spectral component strengths, we develop a clustering-based segmentation method. We apply the proposed method to various point clouds, and analyze their respective spectral properties. Our experimental results demonstrate the effectiveness and efficiency of the proposed segmentation method.

Safety Evaluation of Hyperbolic Arch Aqueduct Using Three-Dimensional Laser Scanning and Finite Element Analysis

The safety evaluation of the aqueduct in many years of operation is often performed to determine the structural operational behaviour so as to provide a scientific basis for further reinforcement or reconstruction. The missing of the original design data due to the long construction period provides great challenging in the structural safety evaluation of the aqueduct. Taking a hyperbolic arch aqueduct in China as an example, we first rebuilt the aqueduct model using the three-dimensional (3D) point cloud from the three-dimensional laser scanning technology. Coupled with the on-site safety inspection, the 3D finite element analysis was then performed to learn the stress performance of the aqueduct body and its supporting structures, so as to achieve the purpose of safety evaluation of aqueduct structure in a whole.

Three‐Dimensional Numerical Modeling and Roof Deformation Analysis of Yuanjue Cave Based on Point Cloud Data

Yuanjue Cave is the core exquisite cultural relic of the Dazu Stone Carvings World Cultural Heritage Site. For hundreds of years, with the continuous effect of natural forces and the erosion of various deterioration factors, the sidewall and roof rock mass of Yuanjue Cave were eroded, some parts of supporting rock mass were fragmented, and the boundary conditions of the rock mass have deteriorated, which have seriously endangered the Yuanjue Cave; once the roof collapses, the national treasure in the cave will be destroyed. In order to preevaluate the stability characteristics of the Yuanjue Cave rock mass and provide key parameters for the preventive protection of the Yuanjue Cave, this paper firstly established a refined database of key parameters of Yuanjue Cave and adjacent areas (geometry, geology, physical properties) based on three‐dimensional laser point cloud scanning, a fine survey of adjacent areas, engineering geophysical prospecting, and indoor multifactor coupling tests. Then, based on FLAC3D finite difference numerical simulation technology, an accurate three‐dimensional numerical calculation model of Yuanjue Cave was constructed. Finally, the model was used to analyze the roof stability of Yuanjue Cavern, revealing the deformation laws of the Yuanjue Cave roof under static load conditions, and the numerical calculation results were compared with the on‐site measured results, verifying the feasibility of the high‐precision modeling method, and the reliability of the numerical calculation results provided a reference for the preventive protection of the cultural relics of the cave temple.

Evaluation of the ICP Algorithm in 3D Point Cloud Registration

The iterative closest point (ICP) algorithm is widely used in three-dimensional (3D) point cloud registration, and it is very stable and robust. However, its biggest drawback is being easily trapped in a local optimal solution, which results in the incorrect registration result. Currently, there is neither a clear effective range to define whether the ICP algorithm will fall into a local optimum nor a study providing a comprehensive evaluation of the ICP algorithm. In this paper, we take the overlap ratio, angle, distance, and noise as the influencing factors of ICP and evaluate the validity, robustness, accuracy, and efficiency of point-to-point and point-to-plane ICP by using four datasets. We first analyze the effective ranges of the two ICP algorithms with respect to overlap ratio, angle, and distance and then propose a universal effective range for the three factors. Next, the effect of Gaussian noise on the validity and accuracy of two ICPs is evaluated. We also analyze the factors influencing ICP accuracy and explain their changing rules. We finally study the effect of different parameters on the efficiency. All results are compared by using point-to-point and point-to-plane ICP algorithms. The results show that the overlap ratio has no effect on validity, but it has a significant influence on accuracy. The angle has a great impact on the validity and efficiency of ICP but has no effect on accuracy. The distance only affects validity, has a limited effect on efficiency and no effect on accuracy. Meanwhile, Gaussian noise has a little effect on the validity. In addition, the general effective range of point-to-point ICP is larger than that of point-to-plane ICP, but the point-to-plane ICP algorithm presents a better efficiency. The point-to-point ICP is more robust to Gaussian noise with respect to validity, while the point-to-plane is more resistant in terms of accuracy.

Building 3D Realistic Modeling Based on Air-ground Multi-source Data Fusion

Three-dimensional point cloud data is a new form of three-dimensional collection, which not only contains the geometric topology information of the object, but also has high simplicity and flexibility. In this paper, the air-ground multi-source data fusion technology is used to study the fine reconstruction of the 3D scene: based on the 3D laser scanning laser point cloud, the 3D spatial information of the ground visible objects is obtained, and the orthophoto obtained by the drone aerial photography is assisted, Obtain the three-dimensional space information of the top of the ground feature, and the ground three-dimensional laser scanner can quickly obtain the three-dimensional surface information of the building facade, ground, and trees. Due to the complex structure of the building and the occlusion of spatial objects, sub-station scanning is required when acquiring point cloud data. This article uses the Sino-German Energy Conservation Center Building of Shenyang Jianzhu University as the research area, using drone tilt photography technology and ground lidar technology to integrate. During the experiment, the field industry adopted the UAV image acquisition strategy of “automatic shooting of regular routes, supplemented by manual shooting of areas of interest”; in the field industry, the method of “manual coarse registration and ICP algorithm fine registration” The example results show that the ground 3D laser point cloud air-ground image fusion 3D modeling effect proposed in this paper is better and the quality is greatly improved, which makes up for the ground 3D laser scanning. In point cloud modeling, a large number of holes are insufficient due to occlusion and missing top information.

Three-dimensional point cloud object segmentation and collision detection based on depth projection

Three-dimensional point cloud object segmentation and collision detection based on depth projection

Construction of three-dimensional point cloud data of work roads using video data.

Construction of three-dimensional point cloud data of work roads using video data.

两阶段变尺度三维点云配准算法研究

两阶段变尺度三维点云配准算法研究