scholarly journals ACCURACY OF 3D RECONSTRUCTION IN AN ILLUMINATION DOME

2016 ◽  
Vol XLI-B5 ◽  
pp. 69-76 ◽  
Author(s):  
Lindsay MacDonald ◽  
Isabella Toschi ◽  
Erica Nocerino ◽  
Mona Hess ◽  
Fabio Remondino ◽  
...  
Keyword(s):  
Surface Reconstruction ◽  
High Frequency ◽  
Point Cloud ◽  
Laser Scanner ◽  
Low Frequency ◽  
Photometric Stereo ◽  
Point Cloud Data ◽  
Cloud Data ◽  
Digital Elevation ◽  
Elevation Model

The accuracy of 3D surface reconstruction was compared from image sets of a Metric Test Object taken in an illumination dome by two methods: photometric stereo and improved structure-from-motion (SfM), using point cloud data from a 3D colour laser scanner as the reference. Metrics included pointwise height differences over the digital elevation model (DEM), and 3D Euclidean differences between corresponding points. The enhancement of spatial detail was investigated by blending high frequency detail from photometric normals, after a Poisson surface reconstruction, with low frequency detail from a DEM derived from SfM.

scholarly journals ACCURACY OF 3D RECONSTRUCTION IN AN ILLUMINATION DOME

2016 ◽  
Vol XLI-B5 ◽  
pp. 69-76
Author(s):  
Lindsay MacDonald ◽  
Isabella Toschi ◽  
Erica Nocerino ◽  
Mona Hess ◽  
Fabio Remondino ◽  
...  
Keyword(s):  
Surface Reconstruction ◽  
High Frequency ◽  
Point Cloud ◽  
Laser Scanner ◽  
Low Frequency ◽  
Photometric Stereo ◽  
Point Cloud Data ◽  
Cloud Data ◽  
Digital Elevation ◽  
Elevation Model

The accuracy of 3D surface reconstruction was compared from image sets of a Metric Test Object taken in an illumination dome by two methods: photometric stereo and improved structure-from-motion (SfM), using point cloud data from a 3D colour laser scanner as the reference. Metrics included pointwise height differences over the digital elevation model (DEM), and 3D Euclidean differences between corresponding points. The enhancement of spatial detail was investigated by blending high frequency detail from photometric normals, after a Poisson surface reconstruction, with low frequency detail from a DEM derived from SfM.

Surface Reconstruction and Nc Simulation of Auto Rearview Mirror Based on Geomagic Studio

2012 ◽  
Vol 271-272 ◽  
pp. 515-518 ◽  
Author(s):  
Huan Lin ◽  
Dong Qiang Gao ◽  
Jiang Miao Yi
Keyword(s):  
Data Processing ◽  
Surface Reconstruction ◽  
Point Cloud ◽  
Laser Scanner ◽  
Point Cloud Data ◽  
Cloud Data ◽  
Data Registration ◽  
Nc Simulation ◽  
Cnc Programming ◽  
Rearview Mirror

The key techniques of reverse engineering include data acquisition, data processing and model reconstruction.In this paper, with the automobile rearview mirror shell for example, scan the rearview mirror shell surface by laser scanner; then carries on the data processing to point cloud data(data processing include point cloud data registration, joining together and polygon stage processing). On the basis of data processing, fitting NURBS surface by Geomagic Studio software, thus completing surface reconstruction; Finally through the NC machining simulation, gets CNC programming, and to make the rearview mirror surface reconstruction and the numerical simulation.

Feature-constrained surface reconstruction approach for point cloud data acquired with 3D laser scanner

2008 ◽  
Author(s):  
Yongbo Wang ◽  
Yehua Sheng ◽  
Guonian Lu ◽  
Peng Tian ◽  
Kai Zhang
Keyword(s):  
Surface Reconstruction ◽  
Point Cloud ◽  
Laser Scanner ◽  
Point Cloud Data ◽  
Cloud Data

From Point Clouds to Surfaces: Overview on a Case Study

2021 ◽  
Author(s):  
Kacper Pluta ◽  
Gisela Domej
Keyword(s):  
Surface Reconstruction ◽  
Point Cloud ◽  
Laser Scanner ◽  
Point Clouds ◽  
Point Cloud Data ◽  
High Quality ◽  
Reconstruction Process ◽  
Cloud Data ◽  
Hill Slope ◽  
Funded Project

<p>The process of transforming point cloud data into high-quality meshes or CAD objects is, in general, not a trivial task. Many problems, such as holes, enclosed pockets, or small tunnels, can occur during the surface reconstruction process, even if the point cloud is of excellent quality. These issues are often difficult to resolve automatically and may require detailed manual adjustments. Nevertheless, in this work, we present a semi-automatic pipeline that requires minimal user-provided input and still allows for high-quality surface reconstruction. Moreover, the presented pipeline can be successfully used by non-specialists and only relies commonly available tools.</p><p>Our pipeline consists of the following main steps: First, a normal field over the point cloud is estimated, and Screened Poisson Surface Reconstruction is applied to obtain the initial mesh. At this stage, the reconstructed mesh usually contains holes, small tunnels, and excess parts – i.e., surface parts that do not correspond to the point cloud geometry. In the next step, we apply morphological and geometrical filtering in order to resolve the problems mentioned before. Some fine details are also removed during the filtration process; however, we show how these can be restored – without reintroducing the problems – using a distance guided projection. In the last step, the filtered mesh is re-meshed to obtain a high-quality triangular mesh, which – if needed – can be converted to a CAD object represented by a small number of quadrangular NURBS patches.</p><p>Our workflow is designed for a point cloud recorded by a laser scanner inside one of seven artificially carved caves resembling chapels with several niches and passages to the outside of a sandstone hill slope in Georgia. We note that we have not tested the approach for other data. Nevertheless, we believe that a similar pipeline can be applied for other types of point cloud data, – e.g., natural caves or mining shafts, geotechnical constructions, rock cliffs, geo-archeological sites, etc. This workflow was created independently, it is not part of a funded project and does not advertise particular software. The case study's point cloud data was used by courtesy of the Dipartimento di Scienze dell'Ambiente e della Terra of the Università degli Studi di Milano–Bicocca.</p>

scholarly journals Direct rapid prototyping from point cloud data without surface reconstruction

2017 ◽  
Vol 15 (3) ◽  
pp. 390-398
Author(s):  
Tianyun Yuan ◽  
Xiaobo Peng ◽  
Dongdong Zhang
Keyword(s):  
Rapid Prototyping ◽  
Surface Reconstruction ◽  
Point Cloud ◽  
Point Cloud Data ◽  
Cloud Data

scholarly journals Rockfall Risk Analysis Based on Precise Topography using UAV

2020 ◽  
Vol 20 (4) ◽  
pp. 63-73
Author(s):  
Jaehee Choi ◽  
Namgyun Kim ◽  
Bongjin Choe ◽  
Byonghee Jun
Keyword(s):  
Point Cloud Data ◽  
University Campus ◽  
Digital Map ◽  
Cloud Data ◽  
The Road ◽  
Mountainous Regions ◽  
Digital Elevation ◽  
Surface Models ◽  
Aerial Vehicle ◽  
Elevation Model

In this study, the risk of rockfall on incision slopes adjacent to roads was evaluated using the RocFall program. The study area was a slope adjacent to the road leading to a university campus in Samcheok-si, Gangwon-do, with an area of 774 m<sup>2</sup> and an average slope of approximately 43°. A rock shed was installed at the lower zone of the slope. A 3D model of the terrain was generated based on point cloud data gathered using a UAV (unmanned aerial vehicle). Fast and accurate orthoimages were captured by UAV and high-resolution digital surface models (DSMs) were produced; these data were used to assess the risk of rockfall. Compared to terrain extraction using a digital elevation model (DEM) generated from an existing digital map, terrain extraction using a UAV was more effective in deriving results close to the actual situation in the field, especially for the analysis of rockfall jump height and kinetic energy. The necessity of constructing 3D topographic data using UAVs to predict rockfall disasters in mountainous regions was confirmed.

scholarly journals On-Site 4-in-1 Alignment: Visualization and Interactive CAD Model Retrofitting Using UAV, LiDAR’s Point Cloud Data, and Video

Sensors ◽  
2019 ◽  
Vol 19 (18) ◽  
pp. 3908 ◽  
Author(s):  
Pavan Kumar B. N. ◽  
Ashok Kumar Patil ◽  
Chethana B. ◽  
Young Ho Chai
Keyword(s):  
Computer Aided Design ◽  
Point Cloud ◽  
Laser Scanner ◽  
Video Frame ◽  
Cad Model ◽  
Point Cloud Data ◽  
Registration Method ◽  
Industrial Facilities ◽  
Cloud Data ◽  
Generic Framework

Acquisition of 3D point cloud data (PCD) using a laser scanner and aligning it with a video frame is a new approach that is efficient for retrofitting comprehensive objects in heavy pipeline industrial facilities. This work contributes a generic framework for interactive retrofitting in a virtual environment and an unmanned aerial vehicle (UAV)-based sensory setup design to acquire PCD. The framework adopts a 4-in-1 alignment using a point cloud registration algorithm for a pre-processed PCD alignment with the partial PCD, and frame-by-frame registration method for video alignment. This work also proposes a virtual interactive retrofitting framework that uses pre-defined 3D computer-aided design models (CAD) with a customized graphical user interface (GUI) and visualization of a 4-in-1 aligned video scene from a UAV camera in a desktop environment. Trials were carried out using the proposed framework in a real environment at a water treatment facility. A qualitative and quantitative study was conducted to evaluate the performance of the proposed generic framework from participants by adopting the appropriate questionnaire and retrofitting task-oriented experiment. Overall, it was found that the proposed framework could be a solution for interactive 3D CAD model retrofitting on a combination of UAV sensory setup-acquired PCD and real-time video from the camera in heavy industrial facilities.

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