scholarly journals Triangulation Reconstruction for 3D Surface Based on Information Model

2016 ◽  
Vol 16 (5) ◽  
pp. 27-33
Author(s):  
Na Li ◽  
Jiquan Yang ◽  
Aiqing Guo ◽  
Yijian Liu ◽  
Hai Liu
Keyword(s):  
Surface Reconstruction ◽  
Point Cloud ◽  
Large Scale ◽  
Low Complexity ◽  
Information Model ◽  
Landslide Monitoring ◽  
Reconstruction Method ◽  
Cloud Data ◽  
3D Surface ◽  
3D Information

Abstract The aim of this paper is to address the surface reconstruction from point cloud in reverser engineering. The data was acquired through a 3D scan device and was processed as point cloud data. The points in cloud were connected to build 3D surface. The points cloud was processed in four steps to get 3D information surface. First, the subtraction scheme was used to get cover boxes ended with the set of convex was found under the convergence rule. Secondly, the points in the box were projected to the directions which were close to the normal direction method. Thirdly the overlap was avoided by using convergence rule and inner subdivision rule. Finally the information model was used to reconstruction. The method was used in landslide monitoring of Three Gorges area for 3D surface reconstruction and monitoring. The reconstruction method obtains high precision and low complexity. It is effective for large scale monitoring.

scholarly journals A 3D Surface Reconstruction Method for Large-Scale Point Cloud Data

2020 ◽  
Vol 2020 ◽  
pp. 1-14
Author(s):  
Baoyun Guo ◽  
Jiawen Wang ◽  
Xiaobin Jiang ◽  
Cailin Li ◽  
Benya Su ◽  
...  
Keyword(s):  
Parallel Computing ◽  
Surface Reconstruction ◽  
Point Cloud ◽  
Large Scale ◽  
Divide And Conquer ◽  
Point Cloud Data ◽  
Cloud Data ◽  
3D Surface Reconstruction ◽  
3D Surface ◽  
Scale Point

Due to the memory limitation and lack of computing power of consumer level computers, there is a need for suitable methods to achieve 3D surface reconstruction of large-scale point cloud data. A method based on the idea of divide and conquer approaches is proposed. Firstly, the kd-tree index was created for the point cloud data. Then, the Delaunay triangulation algorithm of multicore parallel computing was used to construct the point cloud data in the leaf nodes. Finally, the complete 3D mesh model was realized by constrained Delaunay tetrahedralization based on piecewise linear system and graph cut. The proposed method performed surface reconstruction on the point cloud in the multicore parallel computing architecture, in which memory release and reallocation were implemented to reduce the memory occupation and improve the running efficiency while ensuring the quality of the triangular mesh. The proposed algorithm was compared with two classical surface reconstruction algorithms using multigroup point cloud data, and the applicability experiment of the algorithm was carried out; the results verify the effectiveness and practicability of the proposed approach.

scholarly journals A local-optimizing surface reconstruction method based on point cloud data

2020 ◽  
Vol 1605 ◽  
pp. 012065
Author(s):  
Jianwei Ma ◽  
Zhao Liu ◽  
Jiawei Li ◽  
Wenhao Du ◽  
Ziwen Qu ◽  
...  
Keyword(s):  
Surface Reconstruction ◽  
Point Cloud ◽  
Reconstruction Method ◽  
Point Cloud Data ◽  
Cloud Data

scholarly journals A continuous surface reconstruction method on point cloud captured from a 3D surface photogrammetry system

2015 ◽  
Vol 42 (11) ◽  
pp. 6564-6571 ◽  
Author(s):  
Wenyang Liu ◽  
Yam Cheung ◽  
Pouya Sabouri ◽  
Tatsuya J. Arai ◽  
Amit Sawant ◽  
...  
Keyword(s):  
Surface Reconstruction ◽  
Point Cloud ◽  
Reconstruction Method ◽  
3D Surface ◽  
Continuous Surface

scholarly journals A Lightweight Surface Reconstruction Method for Online 3D Scanning Point Cloud Data Oriented toward 3D Printing

2018 ◽  
Vol 2018 ◽  
pp. 1-16 ◽  
Author(s):  
Buyun Sheng ◽  
Feiyu Zhao ◽  
Xiyan Yin ◽  
Chenglei Zhang ◽  
Hui Wang ◽  
...  
Keyword(s):  
3D Printing ◽  
Surface Reconstruction ◽  
Point Cloud ◽  
Reconstruction Algorithm ◽  
3D Scanning ◽  
Reconstruction Method ◽  
Point Cloud Data ◽  
Registration Method ◽  
Cloud Data ◽  
Web Environment

The existing surface reconstruction algorithms currently reconstruct large amounts of mesh data. Consequently, many of these algorithms cannot meet the efficiency requirements of real-time data transmission in a web environment. This paper proposes a lightweight surface reconstruction method for online 3D scanned point cloud data oriented toward 3D printing. The proposed online lightweight surface reconstruction algorithm is composed of a point cloud update algorithm (PCU), a rapid iterative closest point algorithm (RICP), and an improved Poisson surface reconstruction algorithm (IPSR). The generated lightweight point cloud data are pretreated using an updating and rapid registration method. The Poisson surface reconstruction is also accomplished by a pretreatment to recompute the point cloud normal vectors; this approach is based on a least squares method, and the postprocessing of the PDE patch generation was based on biharmonic-like fourth-order PDEs, which effectively reduces the amount of reconstructed mesh data and improves the efficiency of the algorithm. This method was verified using an online personalized customization system that was developed with WebGL and oriented toward 3D printing. The experimental results indicate that this method can generate a lightweight 3D scanning mesh rapidly and efficiently in a web environment.

scholarly journals FUSED 3D TRANSPARENT VISUALIZATION FOR LARGE-SCALE CULTURAL HERITAGE USING DEEP LEARNING-BASED MONOCULAR RECONSTRUCTION

2020 ◽  
Vol V-2-2020 ◽  
pp. 989-996
Author(s):  
J. Pan ◽  
L. Li ◽  
H. Yamaguchi ◽  
K. Hasegawa ◽  
F. I. Thufail ◽  
...  
Keyword(s):  
Deep Learning ◽  
3D Reconstruction ◽  
Cultural Heritage ◽  
Point Cloud ◽  
Large Scale ◽  
Reconstruction Method ◽  
Visualization Method ◽  
Point Cloud Data ◽  
Complete Collection ◽  
Cloud Data

Abstract. This paper proposes a fused 3D transparent visualization method with the aim of achieving see-through imaging of large-scale cultural heritage by combining photogrammetry point cloud data and 3D reconstructed models. 3D reconstructed models are efficiently reconstructed from a single monocular photo using deep learning. It is demonstrated that the proposed method can be widely applied, particularly to instances of incomplete cultural heritages. In this study, the proposed method is applied to a typical example, the Borobudur temple in Indonesia. The Borobudur temple possesses the most complete collection of Buddhist reliefs. However, some parts of the Borobudur reliefs have been hidden by stone walls and became not visible following the reinforcements during the Dutch rule. Today, only gray-scale monocular photos of those hidden parts are displayed in the Borobudur Museum. In this paper, the visible parts of the temple are first digitized into point cloud data by photogrammetry scanning. For the hidden parts, a 3D reconstruction method based on deep learning is proposed to reconstruct the invisible parts into point cloud data directly from single monocular photos from the museum. The proposed 3D reconstruction method achieves 95% accuracy of the reconstructed point cloud on average. With the point cloud data of both the visible parts and the hidden parts, the proposed transparent visualization method called the stochastic point-based rendering is applied to achieve a fused 3D transparent visualization of the valuable temple.

scholarly journals Comparison of Depth Camera and Terrestrial Laser Scanner in Monitoring Structural Deflections

Sensors ◽  
2020 ◽  
Vol 21 (1) ◽  
pp. 201
Author(s):  
Michael Bekele Maru ◽  
Donghwan Lee ◽  
Kassahun Demissie Tola ◽  
Seunghee Park
Keyword(s):  
Optical Sensors ◽  
Point Cloud ◽  
Three Dimensional ◽  
Laser Scanner ◽  
Point Clouds ◽  
Depth Camera ◽  
Terrestrial Laser Scanner ◽  
Cloud Data ◽  
3D Point Clouds ◽  
3D Information

Modeling a structure in the virtual world using three-dimensional (3D) information enhances our understanding, while also aiding in the visualization, of how a structure reacts to any disturbance. Generally, 3D point clouds are used for determining structural behavioral changes. Light detection and ranging (LiDAR) is one of the crucial ways by which a 3D point cloud dataset can be generated. Additionally, 3D cameras are commonly used to develop a point cloud containing many points on the external surface of an object around it. The main objective of this study was to compare the performance of optical sensors, namely a depth camera (DC) and terrestrial laser scanner (TLS) in estimating structural deflection. We also utilized bilateral filtering techniques, which are commonly used in image processing, on the point cloud data for enhancing their accuracy and increasing the application prospects of these sensors in structure health monitoring. The results from these sensors were validated by comparing them with the outputs from a linear variable differential transformer sensor, which was mounted on the beam during an indoor experiment. The results showed that the datasets obtained from both the sensors were acceptable for nominal deflections of 3 mm and above because the error range was less than ±10%. However, the result obtained from the TLS were better than those obtained from the DC.

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