Robust Segmentation in Laser Scanning 3D Point Cloud Data

Structural health monitoring (SHM) and safety assessment are very important areas for evaluating the behavior of structures. Various wired and wireless sensors can measure the physical responses of structures, such as displacement or strain. One recently developed wireless technique is a light imaging detection and ranging (LiDAR) system that can remotely acquire three-dimensional (3D) high-precision coordinate information using 3D laser scanning. LiDAR systems have been previously used in geographic information systems (GIS) to collect information on geography and terrain. Recently, however, LiDAR is used in the SHM field to analyze structural behavior, as it can remotely detect the surface and deformation shape of structures without the need for attached sensors. This study demonstrates a strain evaluation method using a LiDAR system in order to analyze the behavior of steel structures. To evaluate the strains of structures from the initial and deformed shape, a combination of distributed 3D point cloud data and finite element methods (FEM) was used. The distributed 3D point cloud data were reconstructed into a 3D mesh model, and strains were calculated using the FEM. By using the proposed method, the strain could be calculated at any point on a structure for SHM and safety assessment during construction.

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Design and Test of Subway 3D Point Cloud Data Obtained Based on Ground 3D Laser Scanning Technology

Advanced Materials Research ◽

10.4028/www.scientific.net/amr.846-847.981 ◽

2013 ◽

Vol 846-847 ◽

pp. 981-985

Author(s):

Xian Ge Cao ◽

Jin Ling Yang ◽

Dong Hai Li ◽

Jin Yu Feng ◽

Xiang Lai Meng

Keyword(s):

Point Cloud ◽

Laser Scanning ◽

Necessary Conditions ◽

3D Visualization ◽

Target Point ◽

3D Point Cloud ◽

Point Cloud Data ◽

3D Laser Scanning ◽

High Precision Measurement ◽

Cloud Data

3D laser scanning technology can fast, efficient and accurate access high-precision measurement of the target point cloud data and provides the necessary conditions for the development of digital measuring. This paper gives an example of Subway and elaborates the method of 3D point cloud data acquisition, data processing and modeling, and verifies the feasibility of 3D visualization of Subway based on 3D laser.

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BRUTE FORCE MATCHING BETWEEN CAMERA SHOTS AND SYNTHETIC IMAGES FROM POINT CLOUDS

ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences ◽

10.5194/isprsarchives-xli-b5-771-2016 ◽

2016 ◽

Vol XLI-B5 ◽

pp. 771-777 ◽

Cited By ~ 1

Author(s):

R. Boerner ◽

M. Kröhnert

Keyword(s):

Point Cloud ◽

Laser Scanning ◽

Spatial Information ◽

Point Clouds ◽

Synthetic Image ◽

3D Point Cloud ◽

Point Cloud Data ◽

Cloud Data ◽

Force Matching ◽

3D Point Clouds

3D point clouds, acquired by state-of-the-art terrestrial laser scanning techniques (TLS), provide spatial information about accuracies up to several millimetres. Unfortunately, common TLS data has no spectral information about the covered scene. However, the matching of TLS data with images is important for monoplotting purposes and point cloud colouration. Well-established methods solve this issue by matching of close range images and point cloud data by fitting optical camera systems on top of laser scanners or rather using ground control points. <br><br> The approach addressed in this paper aims for the matching of 2D image and 3D point cloud data from a freely moving camera within an environment covered by a large 3D point cloud, e.g. a 3D city model. The key advantage of the free movement affects augmented reality applications or real time measurements. Therefore, a so-called real image, captured by a smartphone camera, has to be matched with a so-called synthetic image which consists of reverse projected 3D point cloud data to a synthetic projection centre whose exterior orientation parameters match the parameters of the image, assuming an ideal distortion free camera.

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BRUTE FORCE MATCHING BETWEEN CAMERA SHOTS AND SYNTHETIC IMAGES FROM POINT CLOUDS

ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences ◽

10.5194/isprs-archives-xli-b5-771-2016 ◽

2016 ◽

Vol XLI-B5 ◽

pp. 771-777

Author(s):

R. Boerner ◽

M. Kröhnert

Keyword(s):

Point Cloud ◽

Laser Scanning ◽

Spatial Information ◽

Point Clouds ◽

Synthetic Image ◽

3D Point Cloud ◽

Point Cloud Data ◽

Cloud Data ◽

Force Matching ◽

3D Point Clouds

3D point clouds, acquired by state-of-the-art terrestrial laser scanning techniques (TLS), provide spatial information about accuracies up to several millimetres. Unfortunately, common TLS data has no spectral information about the covered scene. However, the matching of TLS data with images is important for monoplotting purposes and point cloud colouration. Well-established methods solve this issue by matching of close range images and point cloud data by fitting optical camera systems on top of laser scanners or rather using ground control points. <br><br> The approach addressed in this paper aims for the matching of 2D image and 3D point cloud data from a freely moving camera within an environment covered by a large 3D point cloud, e.g. a 3D city model. The key advantage of the free movement affects augmented reality applications or real time measurements. Therefore, a so-called real image, captured by a smartphone camera, has to be matched with a so-called synthetic image which consists of reverse projected 3D point cloud data to a synthetic projection centre whose exterior orientation parameters match the parameters of the image, assuming an ideal distortion free camera.

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