Three-Dimensional Reconstruction of Single Input Image Based on Point Cloud

2021 ◽  
Vol 87 (7) ◽  
pp. 479-484
Author(s):  
Yu Hou ◽  
Ruifeng Zhai ◽  
Xueyan Li ◽  
Junfeng Song ◽  
Xuehan Ma ◽  
...  
Keyword(s):  
Point Cloud ◽  
Large Scale ◽  
Three Dimensional ◽  
Data Sets ◽  
Point Cloud Data ◽  
Three Dimensional Reconstruction ◽  
Data Set ◽  
Cloud Data ◽  
Dimensional Reconstruction ◽  
Institute Of Technology

Three-dimensional reconstruction from a single image has excellent future prospects. The use of neural networks for three-dimensional reconstruction has achieved remarkable results. Most of the current point-cloud-based three-dimensional reconstruction networks are trained using nonreal data sets and do not have good generalizability. Based on the Karlsruhe Institute of Technology and Toyota Technological Institute at Chicago ()data set of large-scale scenes, this article proposes a method for processing real data sets. The data set produced in this work can better train our network model and realize point cloud reconstruction based on a single picture of the real world. Finally, the constructed point cloud data correspond well to the corresponding three-dimensional shapes, and to a certain extent, the disadvantage of the uneven distribution of the point cloud data obtained by light detection and ranging scanning is overcome using the proposed method.

Improved Three-Dimensional Reconstruction Algorithm for Point Cloud Data

2020 ◽  
Vol 57 (2) ◽  
pp. 021102
Author(s):  
庞正雅 Pang Zhengya ◽  
周志峰 Zhou Zhifeng ◽  
王立端 Wang Liduan ◽  
叶珏磊 Ye Juelei
Keyword(s):  
Point Cloud ◽  
Three Dimensional ◽  
Reconstruction Algorithm ◽  
Point Cloud Data ◽  
Three Dimensional Reconstruction ◽  
Cloud Data ◽  
Dimensional Reconstruction

Three-Dimensional Reconstruction of Leaves Based on Laser Point Cloud Data

2021 ◽  
Author(s):  
Zhonghua Su ◽  
Guiyun Zhou ◽  
Lihui Song ◽  
Xukun Lu ◽  
Rong Zhao ◽  
...  
Keyword(s):  
Point Cloud ◽  
Three Dimensional ◽  
Point Cloud Data ◽  
Three Dimensional Reconstruction ◽  
Cloud Data ◽  
Dimensional Reconstruction

Research on the Parameter Conversion of Mannequin Curve

2014 ◽  
Vol 543-547 ◽  
pp. 2656-2659
Author(s):  
Bo Ren ◽  
Ji Xin Yang ◽  
Peng Wan ◽  
Xue Heng Tao ◽  
Xue Jun Wang ◽  
...  
Keyword(s):  
Point Cloud ◽  
Three Dimensional ◽  
Measuring System ◽  
Reconstruction Method ◽  
Point Cloud Data ◽  
Cloud Data ◽  
Engineering Software ◽  
Parameter Conversion ◽  
Dimensional Reconstruction ◽  
Design Software

In order to realize the reverse design of human bodys curve, the curves parameter conversion and reconstruction based on non-contact measuring system are studied in the paper. Firstly, obtain the model of point cloud data by the non-contact measurement system, and then import the data into reverse the engineering software Geomagic. Second, process the point cloud data with the method of human characteristic curves and surfaces division, structure fitting surface, and get the three-dimensional reconstruction model of human bodys point cloud data. Lastly, import the model into the forward design software Solidworks with different methods and edit it. Then finish the parameter conversion from Geomagic to the forward design software. The reconstruction method has a good value in reverse design of the mold.

scholarly journals Design and Implementation of Substation Operation Safety Monitoring and Management System Based on Three-Dimensional Reconstruction

2020 ◽  
Author(s):  
Yingyi Yang ◽  
Hao Wu ◽  
Fan Yang ◽  
Xiaoming Mai ◽  
Hui Chen
Keyword(s):  
Management System ◽  
Point Cloud ◽  
Control Level ◽  
Three Dimensional ◽  
Safety Monitoring ◽  
Point Cloud Data ◽  
Cloud Data ◽  
Operational Risks ◽  
Dimensional Reconstruction ◽  
3D Scene

In order to reduce operational risks and to improve the risk management and control level in substation, a substation operation safety monitoring and management system (3D2S2M) has been structured based on three-dimensional (3D) laser modeling technology. In this paper, we introduce how to build such a system and to describe its implementation details. A 3D lidar scanning technology is used to perform a holographic scan of the whole internal area in a substation to obtain color point cloud data of buildings and all equipment. Then, a novel 3D visualization safety monitoring and management system, named 3D2S2M, is developed by performing a 3D reconstruction of the point cloud data. Based on the real 3D scene model of 3D2S2M, the method of 3D distance measurement is used to replace manual on-site investigation for improving operation and maintenance efficiency. In addition, a real-time high-accuracy localization method is proposed, in order to identify and analyze the positioning and the behavior of the personnel, and the movement trajectory of the equipment. By combining positioning information and the electronic fence that used in 3D2S2M, risk levels of the personnel (or equipment) are evaluated and the corresponding alarm is issued to prevent dangerous behavior, thereby the operation risk is reduced in substation.

scholarly journals NormNet: Point-wise normal estimation network for three-dimensional point cloud data

2019 ◽  
Vol 16 (4) ◽  
pp. 172988141985753 ◽  
Author(s):  
Janghun Hyeon ◽  
Weonsuk Lee ◽  
Joo Hyung Kim ◽  
Nakju Doh
Keyword(s):  
Point Cloud ◽  
Nearest Neighbor ◽  
Three Dimensional ◽  
Local Feature ◽  
Data Sets ◽  
Point Cloud Data ◽  
K Nearest Neighbor ◽  
Cloud Data ◽  
Normal Estimation ◽  
Local Feature Extraction

In this article, a point-wise normal estimation network for three-dimensional point cloud data called NormNet is proposed. We propose the multiscale K-nearest neighbor convolution module for strengthened local feature extraction. With the multiscale K-nearest neighbor convolution module and PointNet-like architecture, we achieved a hybrid of three features: a global feature, a semantic feature from the segmentation network, and a local feature from the multiscale K-nearest neighbor convolution module. Those features, by mutually supporting each other, not only increase the normal estimation performance but also enable the estimation to be robust under severe noise perturbations or point deficiencies. The performance was validated in three different data sets: Synthetic CAD data (ModelNet), RGB-D sensor-based real 3D PCD (S3DIS), and LiDAR sensor-based real 3D PCD that we built and shared.

scholarly journals THREE-DIMENSIONAL RECORDING OF BASTION MIDDLEBURG MONUMENT USING TERRESTRIAL LASER SCANNER

2016 ◽  
Vol XLI-B5 ◽  
pp. 323-326
Author(s):  
Z. Majid ◽  
C. L. Lau ◽  
A. R. Yusoff
Keyword(s):  
Point Cloud ◽  
Laser Scanning ◽  
Cloud Model ◽  
Three Dimensional ◽  
Third Party ◽  
Scanning System ◽  
Point Cloud Data ◽  
Data Set ◽  
Cloud Data ◽  
Spherical Target

This paper describes the use of terrestrial laser scanning for the full three-dimensional (3D) recording of historical monument, known as the Bastion Middleburg. The monument is located in Melaka, Malaysia, and was built by the Dutch in 1660. This monument serves as a major hub for the community when conducting commercial activities in estuaries Malacca and the Dutch build this monument as a control tower or fortress. The monument is located on the banks of the Malacca River was built between Stadhuys or better known as the Red House and Mill Quayside. The breakthrough fort on 25 November 2006 was a result of the National Heritage Department through in-depth research on the old map. The recording process begins with the placement of measuring targets at strategic locations around the monument. Spherical target was used in the point cloud data registration. The scanning process is carried out using a laser scanning system known as a terrestrial scanner Leica C10. This monument was scanned at seven scanning stations located surrounding the monument with medium scanning resolution mode. Images of the monument have also been captured using a digital camera that is setup in the scanner. For the purposes of proper registration process, the entire spherical target was scanned separately using a high scanning resolution mode. The point cloud data was pre-processed using Leica Cyclone software. The pre-processing process starting with the registration of seven scan data set through overlapping spherical targets. The post-process involved in the generation of coloured point cloud model of the monument using third-party software. The orthophoto of the monument was also produced. This research shows that the method of laser scanning provides an excellent solution for recording historical monuments with true scale of and texture.

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