scholarly journals RECONSTRUCTION OF 3D MODELS FROM POINT CLOUDS WITH HYBRID REPRESENTATION

2018 ◽  
Vol XLII-2 ◽  
pp. 449-454
Author(s):  
P. Hu ◽  
Z. Dong ◽  
P. Yuan ◽  
F. Liang ◽  
B. Yang
Keyword(s):  
3D Reconstruction ◽  
Three Dimensional ◽  
Region Growing ◽  
Reconstruction Algorithm ◽  
Point Clouds ◽  
3D Models ◽  
Building Structures ◽  
Freeform Surfaces ◽  
Irregular Surfaces ◽  
Regular Structures

The three-dimensional (3D) reconstruction of urban buildings from point clouds has long been an active topic in applications related to human activities. However, due to the structures significantly differ in terms of complexity, the task of 3D reconstruction remains a challenging issue especially for the freeform surfaces. In this paper, we present a new reconstruction algorithm which allows the 3D-models of building as a combination of regular structures and irregular surfaces, where the regular structures are parameterized plane primitives and the irregular surfaces are expressed as meshes. The extraction of irregular surfaces starts with an over-segmented method for the unstructured point data, a region growing approach based the adjacent graph of super-voxels is then applied to collapse these super-voxels, and the freeform surfaces can be clustered from the voxels filtered by a thickness threshold. To achieve these regular planar primitives, the remaining voxels with a larger flatness will be further divided into multiscale super-voxels as basic units, and the final segmented planes are enriched and refined in a mutually reinforcing manner under the framework of a global energy optimization. We have implemented the proposed algorithms and mainly tested on two point clouds that differ in point density and urban characteristic, and experimental results on complex building structures illustrated the efficacy of the proposed framework.

scholarly journals Weighing trees with lasers: advances, challenges and opportunities

2018 ◽  
Vol 8 (2) ◽  
pp. 20170048 ◽  
Author(s):  
M. I. Disney ◽  
M. Boni Vicari ◽  
A. Burt ◽  
K. Calders ◽  
S. L. Lewis ◽  
...  
Keyword(s):  
Laser Scanning ◽  
Large Scale ◽  
Three Dimensional ◽  
Point Clouds ◽  
3D Models ◽  
Tree Form ◽  
Form And Function ◽  
Challenges And Opportunities ◽  
And Function ◽  
Non Destructive

Terrestrial laser scanning (TLS) is providing exciting new ways to quantify tree and forest structure, particularly above-ground biomass (AGB). We show how TLS can address some of the key uncertainties and limitations of current approaches to estimating AGB based on empirical allometric scaling equations (ASEs) that underpin all large-scale estimates of AGB. TLS provides extremely detailed non-destructive measurements of tree form independent of tree size and shape. We show examples of three-dimensional (3D) TLS measurements from various tropical and temperate forests and describe how the resulting TLS point clouds can be used to produce quantitative 3D models of branch and trunk size, shape and distribution. These models can drastically improve estimates of AGB, provide new, improved large-scale ASEs, and deliver insights into a range of fundamental tree properties related to structure. Large quantities of detailed measurements of individual 3D tree structure also have the potential to open new and exciting avenues of research in areas where difficulties of measurement have until now prevented statistical approaches to detecting and understanding underlying patterns of scaling, form and function. We discuss these opportunities and some of the challenges that remain to be overcome to enable wider adoption of TLS methods.

scholarly journals Integration, Processing and Dissemination of LiDAR Data in a 3D Web-GIS

2019 ◽  
Vol 8 (3) ◽  
pp. 144 ◽  
Author(s):  
Marek Kulawiak ◽  
Marcin Kulawiak ◽  
Zbigniew Lubniewski
Keyword(s):  
Survey Data ◽  
Three Dimensional ◽  
Point Clouds ◽  
3D Models ◽  
Web Gis ◽  
Data Organization ◽  
3D Model Reconstruction ◽  
Open Standards ◽  
3D Lidar ◽  
The City

The rapid increase in applications of Light Detection and Ranging (LiDAR) scanners, followed by the development of various methods that are dedicated for survey data processing, visualization, and dissemination constituted the need of new open standards for storage and online distribution of collected three-dimensional data. However, over a decade of research in the area has resulted in a number of incompatible solutions that offer their own ways of disseminating results of LiDAR surveys (be it point clouds or reconstructed three-dimensional (3D) models) over the web. The article presents a unified system for remote processing, storage, visualization, and dissemination of 3D LiDAR survey data, including 3D model reconstruction. It is built with the use of open source technologies and employs open standards, such as 3D Tiles, LASer (LAS), and Object (OBJ) for data distribution. The system has been deployed for automatic organization, processing, and dissemination of LiDAR surveys that were performed in the city of Gdansk. The performance of the system has been measured using a selection of LiDAR datasets of various sizes. The system has shown to considerably simplify the process of data organization and integration, while also delivering tools for easy discovery, inspection, and acquisition of desired datasets.

scholarly journals Intact Planar Abstraction of Buildings via Global Normal Refinement from Noisy Oblique Photogrammetric Point Clouds

2018 ◽  
Vol 7 (11) ◽  
pp. 431 ◽  
Author(s):  
Qing Zhu ◽  
Feng Wang ◽  
Han Hu ◽  
Yulin Ding ◽  
Jiali Xie ◽  
...  
Keyword(s):  
Global Optimization ◽  
Three Dimensional ◽  
Region Growing ◽  
Point Clouds ◽  
Spatial Relations ◽  
Global Scale ◽  
Surface Boundary ◽  
Planar Surfaces ◽  
Normal Vectors ◽  
Almost All

Oblique photogrammetric point clouds are currently one of the major data sources for the three-dimensional level-of-detail reconstruction of buildings. However, they are severely noise-laden and pose serious problems for the effective and automatic surface extraction of buildings. In addition, conventional methods generally use normal vectors estimated in a local neighborhood, which are liable to be affected by noise, leading to inferior results in successive building reconstruction. In this paper, we propose an intact planar abstraction method for buildings, which explicitly handles noise by integrating information in a larger context through global optimization. The information propagates hierarchically from a local to global scale through the following steps: first, based on voxel cloud connectivity segmentation, single points are clustered into supervoxels that are enforced to not cross the surface boundary; second, each supervoxel is expanded to nearby supervoxels through the maximal support region, which strictly enforces planarity; third, the relationships established by the maximal support regions are injected into a global optimization, which reorients the local normal vectors to be more consistent in a larger context; finally, the intact planar surfaces are obtained by region growing using robust normal and point connectivity in the established spatial relations. Experiments on the photogrammetric point clouds obtained from oblique images showed that the proposed method is effective in reducing the influence of noise and retrieving almost all of the major planar structures of the examined buildings.

scholarly journals Three-Dimensional Reconstruction of Structural Surface Model of Heritage Bridges Using UAV-Based Photogrammetric Point Clouds

2019 ◽  
Vol 11 (10) ◽  
pp. 1204 ◽  
Author(s):  
Yue Pan ◽  
Yiqing Dong ◽  
Dalei Wang ◽  
Airong Chen ◽  
Zhen Ye
Keyword(s):  
Classification Tree ◽  
Three Dimensional ◽  
Point Clouds ◽  
3D Models ◽  
Surface Model ◽  
Reconstruction Method ◽  
3D Point Clouds ◽  
Surface Models ◽  
Structural Surface ◽  
Better Than

Three-dimensional (3D) digital technology is essential to the maintenance and monitoring of cultural heritage sites. In the field of bridge engineering, 3D models generated from point clouds of existing bridges is drawing increasing attention. Currently, the widespread use of the unmanned aerial vehicle (UAV) provides a practical solution for generating 3D point clouds as well as models, which can drastically reduce the manual effort and cost involved. In this study, we present a semi-automated framework for generating structural surface models of heritage bridges. To be specific, we propose to tackle this challenge via a novel top-down method for segmenting main bridge components, combined with rule-based classification, to produce labeled 3D models from UAV photogrammetric point clouds. The point clouds of the heritage bridge are generated from the captured UAV images through the structure-from-motion workflow. A segmentation method is developed based on the supervoxel structure and global graph optimization, which can effectively separate bridge components based on geometric features. Then, recognition by the use of a classification tree and bridge geometry is utilized to recognize different structural elements from the obtained segments. Finally, surface modeling is conducted to generate surface models of the recognized elements. Experiments using two bridges in China demonstrate the potential of the presented structural model reconstruction method using UAV photogrammetry and point cloud processing in 3D digital documentation of heritage bridges. By using given markers, the reconstruction error of point clouds can be as small as 0.4%. Moreover, the precision and recall of segmentation results using testing date are better than 0.8, and a recognition accuracy better than 0.8 is achieved.

CROWD-SOURCED VISUAL DATA COLLECTION FOR MONITORING INDOOR CONSTRUCTION IN 3D

2018 ◽  
Vol 5 (1) ◽  
Author(s):  
Fouad Amer ◽  
Mani Golparvar-Fard
Keyword(s):  
Data Collection ◽  
3D Reconstruction ◽  
Structure From Motion ◽  
Reconstruction Algorithm ◽  
Point Clouds ◽  
Reconstruction Method ◽  
Visual Data ◽  
Mapping Algorithm ◽  
3D Point Clouds ◽  
Localization And Mapping

Complete and accurate 3D monitoring of indoor construction progress using visual data is challenging. It requires (a) capturing a large number of overlapping images, which is time-consuming and labor-intensive to collect, and (b) processing using Structure from Motion (SfM) algorithms, which can be computationally expensive. To address these inefficiencies, this paper proposes a hybrid SfM-SLAM 3D reconstruction algorithm along with a decentralized data collection workflow to map indoor construction work locations in 3D and any desired frequency. The hybrid 3D reconstruction method is composed of a pipeline of Structure from Motion (SfM) coupled with Multi-View Stereo (MVS) to generate 3D point clouds and a SLAM (Simultaneous Localization and Mapping) algorithm to register the separately formed models together. Our SfM and SLAM pipelines are built on binary Oriented FAST and Rotated BRIEF (ORB) descriptors to tightly couple these two separate reconstruction workflows and enable fast computation. To elaborate the data capture workflow and validate the proposed method, a case study was conducted on a real-world construction site. Compared to state-of-the-art methods, our preliminary results show a decrease in both registration error and processing time, demonstrating the potential of using daily images captured by different trades coupled with weekly walkthrough videos captured by a field engineer for complete 3D visual monitoring of indoor construction operations.

scholarly journals An Integrated Reverse Engineering Approach for Accuracy Control of Free-Form Objects

2016 ◽  
Vol 63 (4) ◽  
pp. 647-663
Author(s):  
Vimal Kumar Pathak ◽  
Chitresh Nayak ◽  
Amit Kumar Singh ◽  
Himanshu Chaudhary
Keyword(s):  
Reverse Engineering ◽  
Three Dimensional ◽  
Fem Analysis ◽  
Point Clouds ◽  
3D Models ◽  
Digital Model ◽  
Surface Generation ◽  
Free Form ◽  
Accuracy Control ◽  
Prosthetic Socket

Abstract Computer-aided tools help in shortening and eradicating numerous repetitive tasks that reduces the gap between digital model and actual product. Use of these tools assists in realizing free-form objects such as custom fit products as described by a stringent interaction with the human body. Development of such a model presents a challenging situation for reverse engineering (RE) which is not analogous with the requirement for generating simple geometric models. Hence, an alternating way of producing more accurate three-dimensional models is proposed. For creating accurate 3D models, point clouds are processed through filtering, segmentation, mesh smoothing and surface generation. These processes help in converting the initial unorganized point data into a 3D digital model and simultaneously influence the quality of model. This study provides an optimum balance for the best accuracy obtainable with maximum allowable deviation to lessen computer handling and processing time. A realistic non trivial case study of free-form prosthetic socket is considered. The accuracy obtained for the developed model is acceptable for the use in medical applications and FEM analysis.

A Simplified 3D Reconstruction Technique for Tomographic Particle Image Velocimetry

2013 ◽  
Vol 718-720 ◽  
pp. 2184-2190
Author(s):  
Bao Quan ◽  
Jiang Nan
Keyword(s):  
Particle Image Velocimetry ◽  
3D Reconstruction ◽  
Particle Image ◽  
Tomographic Reconstruction ◽  
Three Dimensional ◽  
Reconstruction Algorithm ◽  
Tomographic Particle Image Velocimetry ◽  
Image Velocimetry ◽  
3D Volume ◽  
Reconstruction Model

Tomographic particle image velocimetry (Tomo-PIV) is a newly developed technique for three-component three-dimensional (3C-3D) velocity measurement based on the tomographic reconstruction of a 3D volume light intensity field from multiple two-dimensional projections. A simplification of 3D tomographic reconstruction model, which reduced from a 3D volume with 2D images to a 2D slice with 1D lines, simplify this 3D reconstruction into a problem of 2D plane reconstruction by means of optical tomography, is applied in this paper . The principles and details of the tomographic algorithm are discussed, as well as the study of ART and MART reconstruction algorithm is carried out by means of computer-simulated image reconstruction procedure. The three-dimensional volume particle field is reconstructed by MART reconstruction algorithm base on the simplified 3D reconstruction model which made a high reconstruction quality Q=81.37% prove that the way of simplification by MART reconstruction is feasible, so it could be applied in reconstruction of 3D particle field in tomographic particle image velocimetry system.

scholarly journals INTEGRATION OF GEODATA IN DOCUMENTING CASTLE RUINS

2016 ◽  
Vol XLI-B3 ◽  
pp. 345-349
Author(s):  
P. Delis ◽  
M. Wojtkowska ◽  
P. Nerc ◽  
I. Ewiak ◽  
A. Lada
Keyword(s):  
Laser Scanning ◽  
Three Dimensional ◽  
Digital Camera ◽  
Terrestrial Laser Scanning ◽  
Point Clouds ◽  
3D Models ◽  
Digital Imagery ◽  
Historical Structures ◽  
Three Dimensional Models ◽  
Different Sources

Textured three dimensional models are currently the one of the standard methods of representing the results of photogrammetric works. A realistic 3D model combines the geometrical relations between the structure’s elements with realistic textures of each of its elements. Data used to create 3D models of structures can be derived from many different sources. The most commonly used tool for documentation purposes, is a digital camera and nowadays terrestrial laser scanning (TLS). Integration of data acquired from different sources allows modelling and visualization of 3D models historical structures. Additional aspect of data integration is possibility of complementing of missing points for example in point clouds. The paper shows the possibility of integrating data from terrestrial laser scanning with digital imagery and an analysis of the accuracy of the presented methods. The paper describes results obtained from raw data consisting of a point cloud measured using terrestrial laser scanning acquired from a Leica ScanStation2 and digital imagery taken using a Kodak DCS Pro 14N camera. The studied structure is the ruins of the Ilza castle in Poland.

scholarly journals APPLICATIONS OF PANORAMIC IMAGES: FROM 720° PANORAMA TO INTERIOR 3D MODELS OF AUGMENTED REALITY

2015 ◽  
Vol XL-4/W5 ◽  
pp. 189-192 ◽  
Author(s):  
I.-C. Lee ◽  
F. Tsai
Keyword(s):  
Augmented Reality ◽  
Focal Length ◽  
Three Dimensional ◽  
Point Clouds ◽  
3D Models ◽  
Exterior Orientation ◽  
3D Point Clouds ◽  
Panoramic Images ◽  
Main Station ◽  
Orientation Parameters

A series of panoramic images are usually used to generate a 720° panorama image. Although panoramic images are typically used for establishing tour guiding systems, in this research, we demonstrate the potential of using panoramic images acquired from multiple sites to create not only 720° panorama, but also three-dimensional (3D) point clouds and 3D indoor models. Since 3D modeling is one of the goals of this research, the location of the panoramic sites needed to be carefully planned in order to maintain a robust result for close-range photogrammetry. After the images are acquired, panoramic images are processed into 720° panoramas, and these panoramas which can be used directly as panorama guiding systems or other applications. <br><br> In addition to these straightforward applications, interior orientation parameters can also be estimated while generating 720° panorama. These parameters are focal length, principle point, and lens radial distortion. The panoramic images can then be processed with closerange photogrammetry procedures to extract the exterior orientation parameters and generate 3D point clouds. In this research, VisaulSFM, a structure from motion software is used to estimate the exterior orientation, and CMVS toolkit is used to generate 3D point clouds. Next, the 3D point clouds are used as references to create building interior models. In this research, Trimble Sketchup was used to build the model, and the 3D point cloud was added to the determining of locations of building objects using plane finding procedure. In the texturing process, the panorama images are used as the data source for creating model textures. This 3D indoor model was used as an Augmented Reality model replacing a guide map or a floor plan commonly used in an on-line touring guide system. <br><br> The 3D indoor model generating procedure has been utilized in two research projects: a cultural heritage site at Kinmen, and Taipei Main Station pedestrian zone guidance and navigation system. The results presented in this paper demonstrate the potential of using panoramic images to generate 3D point clouds and 3D models. However, it is currently a manual and labor-intensive process. A research is being carried out to Increase the degree of automation of these procedures.

scholarly journals FAST AND ROBUST SEGMENTATION AND CLASSIFICATION FOR CHANGE DETECTION IN URBAN POINT CLOUDS

2016 ◽  
Vol XLI-B3 ◽  
pp. 693-699 ◽  
Author(s):  
X. Roynard ◽  
J.-E. Deschaud ◽  
F. Goulette
Keyword(s):  
Change Detection ◽  
Laser Scanning ◽  
Three Dimensional ◽  
Region Growing ◽  
Point Clouds ◽  
Object Class ◽  
Robust Segmentation ◽  
Street Furniture ◽  
Fast Region

Change detection is an important issue in city monitoring to analyse street furniture, road works, car parking, etc. For example, parking surveys are needed but are currently a laborious task involving sending operators in the streets to identify the changes in car locations. In this paper, we propose a method that performs a fast and robust segmentation and classification of urban point clouds, that can be used for change detection. We apply this method to detect the cars, as a particular object class, in order to perform parking surveys automatically. A recently proposed method already addresses the need for fast segmentation and classification of urban point clouds, using elevation images. The interest to work on images is that processing is much faster, proven and robust. However there may be a loss of information in complex 3D cases: for example when objects are one above the other, typically a car under a tree or a pedestrian under a balcony. In this paper we propose a method that retain the three-dimensional information while preserving fast computation times and improving segmentation and classification accuracy. It is based on fast region-growing using an octree, for the segmentation, and specific descriptors with Random-Forest for the classification. Experiments have been performed on large urban point clouds acquired by Mobile Laser Scanning. They show that the method is as fast as the state of the art, and that it gives more robust results in the complex 3D cases.

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