scholarly journals 3D MODELING OF COMPONENTS OF A GARDEN BY USING POINT CLOUD DATA

2016 ◽  
Vol XLI-B5 ◽  
pp. 305-309
Author(s):  
R. Kumazakia ◽  
Y. Kunii
Keyword(s):  
Point Cloud ◽  
3D Models ◽  
Airborne Lidar ◽  
Point Cloud Data ◽  
Laser Measurement ◽  
Cloud Data ◽  
Model Library ◽  
Japanese Gardens ◽  
The Aesthetic ◽  
Elevation Model

Laser measurement is currently applied to several tasks such as plumbing management, road investigation through mobile mapping systems, and elevation model utilization through airborne LiDAR. Effective laser measurement methods have been well-documented in civil engineering, but few attempts have been made to establish equally effective methods in landscape engineering. By using point cloud data acquired through laser measurement, the aesthetic landscaping of Japanese gardens can be enhanced. This study focuses on simple landscape simulations for pruning and rearranging trees as well as rearranging rocks, lanterns, and other garden features by using point cloud data. However, such simulations lack concreteness. Therefore, this study considers the construction of a library of garden features extracted from point cloud data. The library would serve as a resource for creating new gardens and simulating gardens prior to conducting repairs. Extracted garden features are imported as 3ds Max objects, and realistic 3D models are generated by using a material editor system. As further work toward the publication of a 3D model library, file formats for tree crowns and trunks should be adjusted. Moreover, reducing the size of created models is necessary. Models created using point cloud data are informative because simply shaped garden features such as trees are often seen in the 3D industry.

scholarly journals 3D MODELING OF COMPONENTS OF A GARDEN BY USING POINT CLOUD DATA

2016 ◽  
Vol XLI-B5 ◽  
pp. 305-309
Author(s):  
R. Kumazakia ◽  
Y. Kunii
Keyword(s):  
Point Cloud ◽  
3D Models ◽  
Airborne Lidar ◽  
Point Cloud Data ◽  
Laser Measurement ◽  
Cloud Data ◽  
Model Library ◽  
Japanese Gardens ◽  
The Aesthetic ◽  
Elevation Model

Laser measurement is currently applied to several tasks such as plumbing management, road investigation through mobile mapping systems, and elevation model utilization through airborne LiDAR. Effective laser measurement methods have been well-documented in civil engineering, but few attempts have been made to establish equally effective methods in landscape engineering. By using point cloud data acquired through laser measurement, the aesthetic landscaping of Japanese gardens can be enhanced. This study focuses on simple landscape simulations for pruning and rearranging trees as well as rearranging rocks, lanterns, and other garden features by using point cloud data. However, such simulations lack concreteness. Therefore, this study considers the construction of a library of garden features extracted from point cloud data. The library would serve as a resource for creating new gardens and simulating gardens prior to conducting repairs. Extracted garden features are imported as 3ds Max objects, and realistic 3D models are generated by using a material editor system. As further work toward the publication of a 3D model library, file formats for tree crowns and trunks should be adjusted. Moreover, reducing the size of created models is necessary. Models created using point cloud data are informative because simply shaped garden features such as trees are often seen in the 3D industry.

scholarly journals APPLICATION OF 3D TREE MODELING USING POINT CLOUD DATA BY TERRESTRIAL LASER SCANNER

2020 ◽  
Vol XLIII-B3-2020 ◽  
pp. 995-1000
Author(s):  
R. Kumazaki ◽  
Y. Kunii
Keyword(s):  
Point Cloud ◽  
Laser Scanner ◽  
3D Models ◽  
Point Cloud Data ◽  
Tree Model ◽  
Tree Shape ◽  
Cloud Data ◽  
Tree Modeling ◽  
Natural Diversity ◽  
Japanese Gardens

Abstract. Constructing 3D models for trees such as those found in Japanese gardens, in which many species exist, requires the generation of tree shapes that combine the characteristics of the tree's species and natural diversity. Therefore, this study proposes a method for constructing a 3D tree model with highly-accurate tree shape reproducibility from tree point cloud data acquired by TLS. As a method, we attempted to construct a 3D tree model using the TreeQSM, which is open source for TLS-QSM method. However, in TreeQSM, since processing is based on the assumption that the tree point cloud consists of data related to trunks and branches, measuring trees in which leaves have fallen is recommended. To solve this problem, we proposed an efficient classification process that mainly uses thresholds for deviation and reflectance, which are the adjunct data of the object that can be acquired by laser measurement. Furthermore, to verify accuracy of the model, position coordinates from the constructed 3D tree model were extracted. The extracted coordinates were compared with the those of the tree point cloud data to clarify the extent to which the 3D tree model was constructed from the tree point cloud data. As a result, the 3D tree model was constructed within the standard deviation of 0.016 m from the tree point cloud data. Therefore, the reproducibility of the tree shape by the TLS-QSM method was also effective in terms of accuracy.

scholarly journals General External Uncertainty Models of Three-Plane Intersection Point for 3D Absolute Accuracy Assessment of Lidar Point Cloud

2019 ◽  
Vol 11 (23) ◽  
pp. 2737 ◽  
Author(s):  
Minsu Kim ◽  
Seonkyung Park ◽  
Jeffrey Danielson ◽  
Jeffrey Irwin ◽  
Gregory Stensaas ◽  
...  
Keyword(s):  
Point Cloud ◽  
Accuracy Assessment ◽  
Conjugate Point ◽  
Airborne Lidar ◽  
Lidar Data ◽  
General Function ◽  
Point Cloud Data ◽  
Cloud Data ◽  
Uncertainty Model ◽  
External Uncertainty

The traditional practice to assess accuracy in lidar data involves calculating RMSEz (root mean square error of the vertical component). Accuracy assessment of lidar point clouds in full 3D (three dimension) is not routinely performed. The main challenge in assessing accuracy in full 3D is how to identify a conjugate point of a ground-surveyed checkpoint in the lidar point cloud with the smallest possible uncertainty value. Relatively coarse point-spacing in airborne lidar data makes it challenging to determine a conjugate point accurately. As a result, a substantial unwanted error is added to the inherent positional uncertainty of the lidar data. Unless we keep this additional error small enough, the 3D accuracy assessment result will not properly represent the inherent uncertainty. We call this added error “external uncertainty,” which is associated with conjugate point identification. This research developed a general external uncertainty model using three-plane intersections and accounts for several factors (sensor precision, feature dimension, and point density). This method can be used for lidar point cloud data from a wide range of sensor qualities, point densities, and sizes of the features of interest. The external uncertainty model was derived as a semi-analytical function that takes the number of points on a plane as an input. It is a normalized general function that can be scaled by smooth surface precision (SSP) of a lidar system. This general uncertainty model provides a quantitative guideline on the required conditions for the conjugate point based on the geometric features. Applications of the external uncertainty model were demonstrated using various lidar point cloud data from the U.S. Geological Survey (USGS) 3D Elevation Program (3DEP) library to determine the valid conditions for a conjugate point from three-plane modeling.

scholarly journals AUTOMATIC EXTRACTION AND TOPOLOGY RECONSTRUCTION OF URBAN VIADUCTS FROM LIDAR DATA

2015 ◽  
Vol XL-3/W3 ◽  
pp. 131-135
Author(s):  
Y. Wang ◽  
X. Hu
Keyword(s):  
Point Cloud ◽  
Original Method ◽  
Airborne Lidar ◽  
Least Square ◽  
Automatic Extraction ◽  
Point Cloud Data ◽  
Topological Graph ◽  
Cloud Data ◽  
City Model ◽  
Region Growth

Urban viaducts are important infrastructures for the transportation system of a city. In this paper, an original method is proposed to automatically extract urban viaducts and reconstruct topology of the viaduct network just with airborne LiDAR point cloud data. It will greatly simplify the effort-taking procedure of viaducts extraction and reconstruction. In our method, the point cloud first is filtered to divide all the points into ground points and none-ground points. Region growth algorithm is adopted to find the viaduct points from the none-ground points by the features generated from its general prescriptive designation rules. Then, the viaduct points are projected into 2D images to extract the centerline of every viaduct and generate cubic functions to represent passages of viaducts by least square fitting, with which the topology of the viaduct network can be rebuilt by combining the height information. Finally, a topological graph of the viaducts network is produced. The full-automatic method can potentially benefit the application of urban navigation and city model reconstruction.

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.

scholarly journals A Workflow to Extract the Geometry and Type of Vegetated Landscape Elements from Airborne LiDAR Point Clouds

2021 ◽  
Vol 13 (20) ◽  
pp. 4031
Author(s):  
Ine Rosier ◽  
Jan Diels ◽  
Ben Somers ◽  
Jos Van Orshoven
Keyword(s):  
River Discharge ◽  
Point Cloud ◽  
Point Clouds ◽  
Flood Frequency ◽  
Airborne Lidar ◽  
Main Function ◽  
Point Cloud Data ◽  
Landscape Elements ◽  
Cloud Data ◽  
Landscape Element

Rural European landscapes are characterized by a variety of vegetated landscape elements. Although it is often not their main function, they have the potential to affect river discharge and the frequency, extent, depth and duration of floods downstream by creating both hydrological discontinuities and connections across the landscape. Information about the extent to which individual landscape elements and their spatial location affect peak river discharge and flood frequency and severity in agricultural catchments under specific meteorological conditions is limited. This knowledge gap can partly be explained by the lack of exhaustive inventories of the presence, geometry, and hydrological traits of vegetated landscape elements (vLEs), which in turn is due to the lack of appropriate techniques and source data to produce such inventories and keep them up to date. In this paper, a multi-step methodology is proposed to delineate and classify vLEs based on LiDAR point cloud data in three study areas in Flanders, Belgium. We classified the LiDAR point cloud data into the classes ‘vegetated landscape element point’ and ‘other’ using a Random Forest model with an accuracy classification score ranging between 0.92 and 0.97. The landscape element objects were further classified into the classes ‘tree object’ and ‘shrub object’ using a Logistic Regression model with an area-based accuracy ranging between 0.34 and 0.95.

Sign in / Sign up

Export Citation Format

Share Document