New Approach for Object Detection and Extraction from Digital Images for Providing a 3D Model Applicable in 3D GIS

2019 ◽  
pp. 1324-1349
Author(s):  
Amir Saeed Homainejad
Keyword(s):  
Object Detection ◽  
Point Cloud ◽  
3D Model ◽  
Aerial Images ◽  
Aerial Image ◽  
Point Cloud Data ◽  
3D Gis ◽  
New Approach ◽  
Cloud Data ◽  
3D Space

This paper discusses a new approach in object extraction from aerial images with association of point cloud data. The extracted objects are captured in a 3D space for reconstructing a 3D model. The process includes three steps. In the first step the targeted objects are extracted from point cloud data and captured in a 3D space. The objects include buildings, trees, roads and background or terrain. In the second step the extracted objects are registered to the aerial image for assisting the object detection. Finally, the extracted objects from the aerial image are registered on the original 3D model for conversion to the point cloud data and then are captured in a 3D space for reconstructing a new 3D model. The final 3D model is flexible and editable. The objects can be edited, audited, and manipulated without affecting another objects or ruin the 3D model. Also, more data can be integrated in the 3D model improve its quality. The aspects of this project are: to reconstruct the final 3D model, and then each object can be interactively updated or modified without affecting the whole 3D model, and to provide a database for other users such as 3D GIS, city management and planning, Disaster Management System (DBS), and Smart City application.

New Approach for Object Detection and Extraction from Digital Images for Providing a 3D Model Applicable in 3D GIS

2015 ◽  
Vol 4 (3) ◽  
pp. 34-58
Author(s):  
Amir Saeed Homainejad
Keyword(s):  
Object Detection ◽  
Point Cloud ◽  
3D Model ◽  
Aerial Image ◽  
City Management ◽  
Point Cloud Data ◽  
3D Gis ◽  
New Approach ◽  
Cloud Data ◽  
3D Space

This paper discusses a new approach in object extraction from aerial images with association of point cloud data. The extracted objects are captured in a 3D space for reconstructing a 3D model. The process includes three steps. In the first step the targeted objects are extracted from point cloud data and captured in a 3D space. The objects include buildings, trees, roads and background or terrain. In the second step the extracted objects are registered to the aerial image for assisting the object detection. Finally, the extracted objects from the aerial image are registered on the original 3D model for conversion to the point cloud data and then are captured in a 3D space for reconstructing a new 3D model. The final 3D model is flexible and editable. The objects can be edited, audited, and manipulated without affecting another objects or ruin the 3D model. Also, more data can be integrated in the 3D model improve its quality. The aspects of this project are: to reconstruct the final 3D model, and then each object can be interactively updated or modified without affecting the whole 3D model, and to provide a database for other users such as 3D GIS, city management and planning, Disaster Management System (DBS), and Smart City application.

3D Model Registration Based on Feature Extraction

2011 ◽  
Vol 299-300 ◽  
pp. 1091-1094 ◽  
Author(s):  
Jiang Zhu ◽  
Yuichi Takekuma ◽  
Tomohisa Tanaka ◽  
Yoshio Saito
Keyword(s):  
Feature Extraction ◽  
Point Cloud ◽  
3D Model ◽  
Point Cloud Data ◽  
Coordinate Systems ◽  
High Precision Measurement ◽  
Registration Method ◽  
Cloud Data ◽  
Cad System ◽  
Complicated Model

Currently, design and processing of complicated model are enabled by the progress of the CAD/CAM system. In shape measurement, high precision measurement is performed using CMM. In order to evaluate the machined part, the designed model made by CAD system the point cloud data provided by the measurement system are analyzed and compared. Usually, the designed CAD model and measured point cloud data are made in the different coordinate systems, it is necessary to register those models in the same coordinate system for evaluation. In this research, a 3D model registration method based on feature extraction and iterative closest point (ICP) algorithm is proposed. It could efficiently and accurately register two models in different coordinate systems, and effectively avoid the problem of localized solution.

The Application and Research of Terrestrial 3D Laser Scanning Technology in Tunnel Survey

2014 ◽  
Vol 709 ◽  
pp. 465-468
Author(s):  
Xian Quan Han ◽  
Fei Qin ◽  
Zhen Zhang ◽  
Shang Yi Yang
Keyword(s):  
Point Cloud ◽  
Laser Scanning ◽  
3D Model ◽  
Analysis Procedure ◽  
Surface Model ◽  
Point Cloud Data ◽  
3D Laser Scanning ◽  
Cloud Data ◽  
Experimental Application ◽  
Tunnel Section

This paper examines the basic flow and processing of the terrestrial 3D Laser scanning technology in the tunnel survey. The use of the method is discussed, point cloud data which have been registered, cropped can be constructed to a complete tunnel surface model. An example is given to extract the tunnel section and calculate the excavation of the tunnel. Result of the experimental application of this analysis procedure is given to illustrate the proposed technique can be flexibly used according to the need based on its 3D model. The feasibility and advantages of terrestrial 3D laser scanning technology in tunnel survey is also considered.

Reverse Modeling of Vehicle Suspension Control Arm

2013 ◽  
Vol 427-429 ◽  
pp. 1183-1186
Author(s):  
Hai Bo Zhang ◽  
Ying Chang ◽  
Rui Jun Zhang ◽  
Hong Yuan Fan
Keyword(s):  
Point Cloud ◽  
Production Cost ◽  
3D Model ◽  
Modeling Method ◽  
Curved Surface ◽  
Vehicle Suspension ◽  
Point Cloud Data ◽  
Cloud Data ◽  
Suspension Control ◽  
Curved Surface Parts

Reverse modeling of vehicle suspension control arm is studied. Firstly, the data of vehicle suspension control arm is acquired. Secondly, the scanning point cloud data is processed. Then 3D model of vehicle suspension control arm is reconstructed. Finally, models deviation is analyzed to find out the deficiencies and to improve the accuracy of the model. Reverse modeling based on CATIA can make up the shortage of traditional modeling method and solve the problem of gaining the data of complex curved surface parts. It also can shorten the innovation and improvement cycle of the vehicle suspension control arm and reduce the production cost and enhance enterprise competitiveness.

scholarly journals Co-Segmentation and Superpixel-Based Graph Cuts for Building Change Detection from Bi-Temporal Digital Surface Models and Aerial Images

2019 ◽  
Vol 11 (6) ◽  
pp. 729 ◽  
Author(s):  
Shiyan Pang ◽  
Xiangyun Hu ◽  
Mi Zhang ◽  
Zhongliang Cai ◽  
Fengzhu Liu
Keyword(s):  
Change Detection ◽  
Point Cloud ◽  
Laser Scanner ◽  
Graph Cuts ◽  
Aerial Images ◽  
Processing Unit ◽  
3D Point Cloud ◽  
Point Cloud Data ◽  
Cloud Data ◽  
Surface Models

Thanks to the recent development of laser scanner hardware and the technology of dense image matching (DIM), the acquisition of three-dimensional (3D) point cloud data has become increasingly convenient. However, how to effectively combine 3D point cloud data and images to realize accurate building change detection is still a hotspot in the field of photogrammetry and remote sensing. Therefore, with the bi-temporal aerial images and point cloud data obtained by airborne laser scanner (ALS) or DIM as the data source, a novel building change detection method combining co-segmentation and superpixel-based graph cuts is proposed in this paper. In this method, the bi-temporal point cloud data are firstly combined to achieve a co-segmentation to obtain bi-temporal superpixels with the simple linear iterative clustering (SLIC) algorithm. Secondly, for each period of aerial images, semantic segmentation based on a deep convolutional neural network is used to extract building areas, and this is the basis for subsequent superpixel feature extraction. Again, with the bi-temporal superpixel as the processing unit, a graph-cuts-based building change detection algorithm is proposed to extract the changed buildings. In this step, the building change detection problem is modeled as two binary classifications, and acquisition of each period’s changed buildings is a binary classification, in which the changed building is regarded as foreground and the other area as background. Then, the graph cuts algorithm is used to obtain the optimal solution. Next, by combining the bi-temporal changed buildings and digital surface models (DSMs), these changed buildings are further classified as “newly built,” “taller,” “demolished”, and “lower”. Finally, two typical datasets composed of bi-temporal aerial images and point cloud data obtained by ALS or DIM are used to validate the proposed method, and the experiments demonstrate the effectiveness and generality of the proposed algorithm.

Design and Rapid Prototyping of S-Shape Push-Ups Frame Based on Laser Scanning

2015 ◽  
Vol 752-753 ◽  
pp. 1401-1405 ◽  
Author(s):  
Hong Jun Ni ◽  
Qing Qing Chen ◽  
Yi Pei ◽  
Yi Lv ◽  
Xing Xing Wang
Keyword(s):  
Rapid Prototyping ◽  
Point Cloud ◽  
Laser Scanning ◽  
3D Model ◽  
Laser Scanner ◽  
Point Cloud Data ◽  
Model Design ◽  
Cloud Data ◽  
Frame Model ◽  
Business Efficiency

Model design and rapid prototyping are utilized to manufacture push-ups frame. Point cloud data can be obtained by scanning parts with hand-held laser scanner, and imported into the Imageware to process. The varied points are removed, the missing points are repaired, and then the 3D model is designed through the Pro/E. Finally, the frame model is completed by rapid prototyping printers. The manufacturing period is shorten through the way of putting two technologies in the field of manufacturing together, the production requirements are met, and the business efficiency is improved.

3D Model Reconstruction of the Broken Aeroengine Blade Based on the Laplacian of Guassian Detection

2012 ◽  
Vol 490-495 ◽  
pp. 143-146
Author(s):  
Miao Gong ◽  
Hao Wang ◽  
Li Wen Wang
Keyword(s):  
Point Cloud ◽  
Measuring Equipment ◽  
3D Model ◽  
Digital Model ◽  
Point Cloud Data ◽  
Model Reconstruction ◽  
Cloud Data ◽  
3D Model Reconstruction ◽  
Reduced Time ◽  
3D Digital Model

This paper made the 3D model reconstruction of the J34 turban blade. First, collected rough points cloud data by using visual measuring equipment. Then, smoothed and filtered the point cloud data, took the rational simplification, finished pre-processing the point cloud data. Finally, the Laplacian of Guassian Detection was used for fitting the edge of turban blade, and reconstructed the 3D digital model. The results proved that this method improved smoothness of the model, and reduced time and cost of modeling and machining.

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