scholarly journals RegARD: Symmetry-Based Coarse Registration of Smartphone’s Colorful Point Clouds with CAD Drawings for Low-Cost Digital Twin Buildings

2021 ◽  
Vol 13 (10) ◽  
pp. 1882
Author(s):  
Yijie Wu ◽  
Jianga Shang ◽  
Fan Xue
Keyword(s):  
Degrees Of Freedom ◽  
Low Cost ◽  
Point Clouds ◽  
Data Sets ◽  
Digital Twin ◽  
3D Point Clouds ◽  
Coarse Registration ◽  
Smartphone Sensors ◽  
Novel Method ◽  
Twin Buildings

Coarse registration of 3D point clouds plays an indispensable role for parametric, semantically rich, and realistic digital twin buildings (DTBs) in the practice of GIScience, manufacturing, robotics, architecture, engineering, and construction. However, the existing methods have prominently been challenged by (i) the high cost of data collection for numerous existing buildings and (ii) the computational complexity from self-similar layout patterns. This paper studies the registration of two low-cost data sets, i.e., colorful 3D point clouds captured by smartphones and 2D CAD drawings, for resolving the first challenge. We propose a novel method named `Registration based on Architectural Reflection Detection’ (RegARD) for transforming the self-symmetries in the second challenge from a barrier of coarse registration to a facilitator. First, RegARD detects the innate architectural reflection symmetries to constrain the rotations and reduce degrees of freedom. Then, a nonlinear optimization formulation together with advanced optimization algorithms can overcome the second challenge. As a result, high-quality coarse registration and subsequent low-cost DTBs can be created with semantic components and realistic appearances. Experiments showed that the proposed method outperformed existing methods considerably in both effectiveness and efficiency, i.e., 49.88% less error and 73.13% less time, on average. The RegARD presented in this paper first contributes to coarse registration theories and exploitation of symmetries and textures in 3D point clouds and 2D CAD drawings. For practitioners in the industries, RegARD offers a new automatic solution to utilize ubiquitous smartphone sensors for massive low-cost DTBs.

scholarly journals INTEGRATION OF 3D OBJECTS AND TERRAIN FOR 3D MODELLING SUPPORTING THE DIGITAL TWIN

2019 ◽  
Vol IV-4/W8 ◽  
pp. 147-154 ◽  
Author(s):  
J. Yan ◽  
S. Zlatanova ◽  
M. Aleksandrov ◽  
A. A. Diakite ◽  
C. Pettit
Keyword(s):  
Point Clouds ◽  
Critical Issue ◽  
3D Modelling ◽  
Data Sets ◽  
Military Operations ◽  
3D City Models ◽  
Digital Twin ◽  
3D Objects ◽  
3D Point Clouds ◽  
City Models

<p><strong>Abstract.</strong> 3D modelling of precincts and cities has significantly advanced in the last decades, as we move towards the concept of the Digital Twin. Many 3D city models have been created but a large portion of them neglect representing terrain and buildings accurately. Very often the surface is either considered planar or is not represented. On the other hand, many Digital Terrain Models (DTM) have been created as 2.5D triangular irregular networks (TIN) or grids for different applications such as water management, sign of view or shadow computation, tourism, land planning, telecommunication, military operations and communications. 3D city models need to represent both the 3D objects and terrain in one consistent model, but still many challenges remain. A critical issue when integrating 3D objects and terrain is the identification of the valid intersection between 2.5D terrain and 3D objects. Commonly, 3D objects may partially float over or sink into the terrain; the depth of the underground parts might not be known; or the accuracy of data sets might be different. This paper discusses some of these issues and presents an approach for a consistent 3D reconstruction of LOD1 models on the basis of 3D point clouds, DTM, and 2D footprints of buildings. Such models are largely used for urban planning, city analytics or environmental analysis. The proposed method can be easily extended for higher LODs or BIM models.</p>

scholarly journals Estimating tree stem diameters and volume from smartphone photogrammetric point clouds

2019 ◽  
Vol 93 (3) ◽  
pp. 411-429 ◽  
Author(s):  
Maria Immacolata Marzulli ◽  
Pasi Raumonen ◽  
Roberto Greco ◽  
Manuela Persia ◽  
Patrizia Tartarino
Keyword(s):  
Low Cost ◽  
Three Dimensional ◽  
Laser Scanner ◽  
Point Clouds ◽  
Stem Volume ◽  
Forest Plot ◽  
3D Point Clouds ◽  
Dense Point ◽  
Minimum Number ◽  
Scale Point

Abstract Methods for the three-dimensional (3D) reconstruction of forest trees have been suggested for data from active and passive sensors. Laser scanner technologies have become popular in the last few years, despite their high costs. Since the improvements in photogrammetric algorithms (e.g. structure from motion—SfM), photographs have become a new low-cost source of 3D point clouds. In this study, we use images captured by a smartphone camera to calculate dense point clouds of a forest plot using SfM. Eighteen point clouds were produced by changing the densification parameters (Image scale, Point density, Minimum number of matches) in order to investigate their influence on the quality of the point clouds produced. In order to estimate diameter at breast height (d.b.h.) and stem volumes, we developed an automatic method that extracts the stems from the point cloud and then models them with cylinders. The results show that Image scale is the most influential parameter in terms of identifying and extracting trees from the point clouds. The best performance with cylinder modelling from point clouds compared to field data had an RMSE of 1.9 cm and 0.094 m3, for d.b.h. and volume, respectively. Thus, for forest management and planning purposes, it is possible to use our photogrammetric and modelling methods to measure d.b.h., stem volume and possibly other forest inventory metrics, rapidly and without felling trees. The proposed methodology significantly reduces working time in the field, using ‘non-professional’ instruments and automating estimates of dendrometric parameters.

scholarly journals Localization of Windows and Doors in 3d Point Clouds of Facades

2014 ◽  
Vol II-3 ◽  
pp. 87-94 ◽  
Author(s):  
W. Nguatem ◽  
M. Drauschke ◽  
H. Mayer
Keyword(s):  
Bayes Factor ◽  
Likelihood Function ◽  
Parallel Implementation ◽  
Point Clouds ◽  
Search Space ◽  
Shape Space ◽  
Data Sets ◽  
3D Point Clouds ◽  
Spline Curves ◽  
Search Routine

In this paper, we present a fully automatic approach to localize the outlines of facade objects (windows and doors) in 3D point clouds of facades. We introduce an approach to search for the main facade wall and locate the facade objects within a probabilistic framework. Our search routine is based on Monte Carlo Simulation (MC-Simulation). Templates containing control points of curves are used to approximate the possible shapes of windows and doors. These are interpolated using parametric B-spline curves. These templates are scored in a sliding window style over the entire facade using a likelihood function in a probabilistic matching procedure. This produces many competing results for which a two layered model selection based on Bayes factor is applied. A major thrust in our work is the introduction of a 2D shape-space of similar shapes under affine transform in this architectural scene. This transforms the initial parametric B-splines curves representing the outlines of objects to curves of affine similarity in a strongly reduced dimensionality thus facilitating the generation of competing hypotheses within the search space. A further computational speedup is achieved through the clustering of the search space to disjoint regions, thus enabling a parallel implementation. We obtain state-of-the results on self-acquired data sets. The robustness of our algorithm is evaluated on 3D point clouds from image matching and LiDAR data of diverse quality.

scholarly journals USING MOBILE LASER SCANNING DATA FOR FEATURES EXTRACTION OF HIGH ACCURACY DRIVING MAPS

2016 ◽  
Vol XLI-B3 ◽  
pp. 433-439 ◽  
Author(s):  
Bisheng Yang ◽  
Yuan Liu ◽  
Fuxun Liang ◽  
Zhen Dong
Keyword(s):  
Laser Scanning ◽  
Human Error ◽  
Traffic Accidents ◽  
Three Dimensional ◽  
Point Clouds ◽  
High Accuracy ◽  
3D Point Clouds ◽  
Assistant Systems ◽  
Novel Method ◽  
High Flexibility

High Accuracy Driving Maps (HADMs) are the core component of Intelligent Drive Assistant Systems (IDAS), which can effectively reduce the traffic accidents due to human error and provide more comfortable driving experiences. Vehicle-based mobile laser scanning (MLS) systems provide an efficient solution to rapidly capture three-dimensional (3D) point clouds of road environments with high flexibility and precision. This paper proposes a novel method to extract road features (e.g., road surfaces, road boundaries, road markings, buildings, guardrails, street lamps, traffic signs, roadside-trees, power lines, vehicles and so on) for HADMs in highway environment. Quantitative evaluations show that the proposed algorithm attains an average precision and recall in terms of 90.6% and 91.2% in extracting road features. Results demonstrate the efficiencies and feasibilities of the proposed method for extraction of road features for HADMs.

scholarly journals ACCURACY ASSESSMENT OF UNDERWATER PHOTOGRAMMETRIC THREE DIMENSIONAL MODELLING FOR CORAL REEFS

2016 ◽  
Vol XLI-B5 ◽  
pp. 821-828 ◽  
Author(s):  
T. Guo ◽  
A. Capra ◽  
M. Troyer ◽  
A. Gruen ◽  
A. J. Brooks ◽  
...  
Keyword(s):  
Coral Cover ◽  
Accuracy Assessment ◽  
Low Cost ◽  
Three Dimensional ◽  
Point Clouds ◽  
3D Modelling ◽  
Relative Accuracy ◽  
3D Point Clouds ◽  
The Individual ◽  
Working Distance

Recent advances in automation of photogrammetric 3D modelling software packages have stimulated interest in reconstructing highly accurate 3D object geometry in unconventional environments such as underwater utilizing simple and low-cost camera systems. The accuracy of underwater 3D modelling is affected by more parameters than in single media cases. This study is part of a larger project on 3D measurements of temporal change of coral cover in tropical waters. It compares the accuracies of 3D point clouds generated by using images acquired from a system camera mounted in an underwater housing and the popular GoPro cameras respectively. A precisely measured calibration frame was placed in the target scene in order to provide accurate control information and also quantify the errors of the modelling procedure. In addition, several objects (cinder blocks) with various shapes were arranged in the air and underwater and 3D point clouds were generated by automated image matching. These were further used to examine the relative accuracy of the point cloud generation by comparing the point clouds of the individual objects with the objects measured by the system camera in air (the best possible values). Given a working distance of about 1.5 m, the GoPro camera can achieve a relative accuracy of 1.3 mm in air and 2.0 mm in water. The system camera achieved an accuracy of 1.8 mm in water, which meets our requirements for coral measurement in this system.

A novel method for extracting skeleton of fruit tree from 3D point clouds

2020 ◽  
pp. 2050051
Author(s):  
Shenglian lu ◽  
Guo Li ◽  
Jian Wang
Keyword(s):  
Laser Scanning ◽  
Laser Scanner ◽  
Point Clouds ◽  
Fruit Trees ◽  
Fruit Tree ◽  
Tree Canopy ◽  
3D Point Clouds ◽  
Novel Method ◽  
Manual Interaction ◽  
Traditional Approaches

Tree skeleton could be useful to agronomy researchers because the skeleton describes the shape and topological structure of a tree. The phenomenon of organs’ mutual occlusion in fruit tree canopy is usually very serious, this should result in a large amount of data missing in directed laser scanning 3D point clouds from a fruit tree. However, traditional approaches can be ineffective and problematic in extracting the tree skeleton correctly when the tree point clouds contain occlusions and missing points. To overcome this limitation, we present a method for accurate and fast extracting the skeleton of fruit tree from laser scanner measured 3D point clouds. The proposed method selects the start point and endpoint of a branch from the point clouds by user’s manual interaction, then a backward searching is used to find a path from the 3D point cloud with a radius parameter as a restriction. The experimental results in several kinds of fruit trees demonstrate that our method can extract the skeleton of a leafy fruit tree with highly accuracy.

scholarly journals DIGITAL TERRAIN MODELS FROM MOBILE LASER SCANNING DATA IN MORAVIAN KARST

2016 ◽  
Vol XLI-B3 ◽  
pp. 387-394 ◽  
Author(s):  
N. Tyagur ◽  
M. Hollaus
Keyword(s):  
Czech Republic ◽  
Laser Scanning ◽  
Point Clouds ◽  
Low Noise ◽  
Data Sets ◽  
The Czech Republic ◽  
Digital Terrain ◽  
3D Point Clouds ◽  
Moravian Karst

During the last ten years, mobile laser scanning (MLS) systems have become a very popular and efficient technology for capturing reality in 3D. A 3D laser scanner mounted on the top of a moving vehicle (e.g. car) allows the high precision capturing of the environment in a fast way. Mostly this technology is used in cities for capturing roads and buildings facades to create 3D city models. In our work, we used an MLS system in Moravian Karst, which is a protected nature reserve in the Eastern Part of the Czech Republic, with a steep rocky terrain covered by forests. For the 3D data collection, the Riegl VMX 450, mounted on a car, was used with integrated IMU/GNSS equipment, which provides low noise, rich and very dense 3D point clouds. <br><br> The aim of this work is to create a digital terrain model (DTM) from several MLS data sets acquired in the neighbourhood of a road. The total length of two covered areas is 3.9 and 6.1 km respectively, with an average width of 100 m. For the DTM generation, a fully automatic, robust, hierarchic approach was applied. The derivation of the DTM is based on combinations of hierarchical interpolation and robust filtering for different resolution levels. For the generation of the final DTMs, different interpolation algorithms are applied to the classified terrain points. The used parameters were determined by explorative analysis. All MLS data sets were processed with one parameter set. As a result, a high precise DTM was derived with high spatial resolution of 0.25 x 0.25 m. The quality of the DTMs was checked by geodetic measurements and visual comparison with raw point clouds. The high quality of the derived DTM can be used for analysing terrain changes and morphological structures. Finally, the derived DTM was compared with the DTM of the Czech Republic (DMR 4G) with a resolution of 5 x 5 m, which was created from airborne laser scanning data. The vertical accuracy of the derived DTMs is around 0.10 m.

scholarly journals A 2.5D APPROACH TO SKIN WRINKLES SEGMENTATION

2019 ◽  
Vol 38 (1) ◽  
pp. 75 ◽  
Author(s):  
Etienne Decencière ◽  
Amira Belhedi ◽  
Serge Koudoro ◽  
Frédéric Flament ◽  
Ghislain François ◽  
...  
Keyword(s):  
Point Clouds ◽  
Skin Surface ◽  
Fringe Projection ◽  
Physical Parameters ◽  
Projection System ◽  
Beneficial Effects ◽  
3D Point Clouds ◽  
Novel Method ◽  
Skin Wrinkles ◽  
Skin Wrinkling

Wrinkles or creases are common structures on surfaces. Their detection is often challenging, and can be an important step for many different applications. For instance, skin wrinkle segmentation is a crucial step for quantifying changes in skin wrinkling and assessing the beneficial effects of dermatological and cosmetic anti-ageing treatments. A 2.5D approach is proposed in this paper to segment individual wrinkles on facial skin surface described by 3D point clouds. The method, based on mathematical morphology, only needs a few physical parameters as input, namely the maximum wrinkle width, the minimum wrinkle length, and the minimum wrinkle depth. It has been applied to data acquired from eye wrinkles using a fringe projection system. An accurate evaluation was made possible thanks to manual annotations provided by three different experts. Results demonstrate the accuracy of this novel method.

scholarly journals MULTI-SCALE BASED EXTRACION OF VEGETATION FROM TERRESTRIAL LiDAR DATA FOR ASSESSING LOCAL LANDSCAPE

2015 ◽  
Vol II-3/W4 ◽  
pp. 263-270 ◽  
Author(s):  
T. Wakita ◽  
J. Susaki
Keyword(s):  
Urban Areas ◽  
Three Dimensional ◽  
Point Clouds ◽  
Lidar Data ◽  
Data Sets ◽  
Landscape Index ◽  
Terrestrial Lidar ◽  
Multi Scale ◽  
3D Point Clouds ◽  
Set Of Points

In this study, we propose a method to accurately extract vegetation from terrestrial three-dimensional (3D) point clouds for estimating landscape index in urban areas. Extraction of vegetation in urban areas is challenging because the light returned by vegetation does not show as clear patterns as man-made objects and because urban areas may have various objects to discriminate vegetation from. The proposed method takes a multi-scale voxel approach to effectively extract different types of vegetation in complex urban areas. With two different voxel sizes, a process is repeated that calculates the eigenvalues of the planar surface using a set of points, classifies voxels using the approximate curvature of the voxel of interest derived from the eigenvalues, and examines the connectivity of the valid voxels. We applied the proposed method to two data sets measured in a residential area in Kyoto, Japan. The validation results were acceptable, with F-measures of approximately 95% and 92%. It was also demonstrated that several types of vegetation were successfully extracted by the proposed method whereas the occluded vegetation were omitted. We conclude that the proposed method is suitable for extracting vegetation in urban areas from terrestrial light detection and ranging (LiDAR) data. In future, the proposed method will be applied to mobile LiDAR data and the performance of the method against lower density of point clouds will be examined.

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