Three-dimensional point cloud based sky view factor analysis in complex urban settings

2013 ◽  
Vol 34 (8) ◽  
pp. 2685-2701 ◽  
Author(s):  
S. M. An ◽  
B. S. Kim ◽  
H. Y. Lee ◽  
C. H. Kim ◽  
C. Y. Yi ◽  
...  
Keyword(s):  
Factor Analysis ◽  
Point Cloud ◽  
Three Dimensional ◽  
View Factor ◽  
Urban Settings ◽  
Sky View Factor

Three-Dimensional Point Cloud Registration Algorithm Based on Factor Analysis

2019 ◽  
Vol 56 (19) ◽  
pp. 191503
Author(s):  
唐志荣 Tang Zhirong ◽  
蒋悦 Jiang Yue ◽  
苗长伟 Miao Changwei ◽  
赵成强 Zhao Chengqiang
Keyword(s):  
Factor Analysis ◽  
Point Cloud ◽  
Three Dimensional ◽  
Point Cloud Registration ◽  
Registration Algorithm

scholarly journals V-Factor Indicator in the Assessment of the Change in the Attractiveness of View as a Result of the Implementation of a Specific Planning Scenario

2019 ◽  
Vol 8 (2) ◽  
pp. 78
Author(s):  
Magda Pluta ◽  
Bartosz Mitka
Keyword(s):  
Factor Analysis ◽  
Dimensional Space ◽  
Three Dimensional ◽  
Observation Point ◽  
View Factor ◽  
Factor V ◽  
Average Value ◽  
3D Space ◽  
The Aesthetic ◽  
Special Points

This paper describes the algorithm of the view factor (V-factor). It is based on an analysis of visibility, taking into account the attractiveness of the observed elements in a three-dimensional space. The results of the V-factor analysis provide input for the decision-making process when selecting the most advantageous planning scenario so that the harmony of landscape and ecological balance can be maintained. The V-factor indicator can be successfully used in the process of spatial planning, in particular, at the stage of determining the parameters of new buildings and lines of sight between planned buildings. The purpose of the indicator is to determine the numerical values for observation points, thus facilitating a comparative assessment of the attractiveness of view available from the special points in space. The analysis uses a 3D space model that includes an integrated existing and planning state designed on the basis of planning scenarios. The V-factor analysis takes into account the distance of the observation point from the observed object, vertical and horizontal angles of observation, and the aesthetic value of the observed object. As a result, an average value of the V-factor indicator was obtained for each planning scenario, which facilitated the determination of the more beneficial one in terms of the attractiveness of view.

scholarly journals BUDDHIST STELE OF SWAT VALLEY: POINT CLOUD ANALYSIS AND INTERPRETATION

2019 ◽  
Vol XLII-2/W18 ◽  
pp. 31-37
Author(s):  
E. Faresin ◽  
G. Salemi
Keyword(s):  
Point Cloud ◽  
High Speed ◽  
High Accuracy ◽  
View Factor ◽  
3D Measurement ◽  
Sky View Factor ◽  
Artificial Illumination ◽  
Local Relief ◽  
Swat Valley ◽  
Point Cloud Analysis

Abstract. With recent advancements on 3D sensors and cloud computing, high-speed, high-accuracy 3D measurement at micrometer level have been increase by scientists community and digital humanities researchers.The methodology proposed in this project aims to test some of the algorithms used in remote-sensing to the Buddhist sculptures from Swat Valley (Pakistan); these algorithms use high-resolution topographic data to identify, from DEMs, specific features like valleys, ridges, peaks, pits or surface anomalies.In the carved stone, the surface is analysed like a landscape, where carved areas are valleys bordered by slopes and crests. One of the simplest tools, the commonly used analytical hill-shading, which simulates artificial illumination on the DEM surface, is based on the same principle as the use of an oblique light source to highlight incisions in classic photography. Other families of algorithms that can be divided into three main groups (Slope and Curvature, Local Relief Model and Sky View Factor, Positive and Negative Openness and Geomorphons) are tested here.

Handheld Laser Scanner Research

2019 ◽  
Vol 952 (10) ◽  
pp. 47-54
Author(s):  
A.V. Komissarov ◽  
A.V. Remizov ◽  
M.M. Shlyakhova ◽  
K.K. Yambaev
Keyword(s):  
Point Cloud ◽  
Three Dimensional ◽  
Laser Scanner ◽  
Test Site ◽  
Optical Systems ◽  
Advantages And Disadvantages ◽  
Site Survey ◽  
Laser Scanners ◽  
Three Dimensional Models ◽  
The Stability

The authors consider hand-held laser scanners, as a new photogrammetric tool for obtaining three-dimensional models of objects. The principle of their work and the newest optical systems based on various sensors measuring the depth of space are described in detail. The method of simultaneous navigation and mapping (SLAM) used for combining single scans into point cloud is outlined. The formulated tasks and methods for performing studies of the DotProduct (USA) hand-held laser scanner DPI?8X based on a test site survey are presented. The accuracy requirements for determining the coordinates of polygon points are given. The essence of the performed experimental research of the DPI?8X scanner is described, including scanning of a test object at various scanner distances, shooting a test polygon from various scanner positions and building point cloud, repeatedly shooting the same area of the polygon to check the stability of the scanner. The data on the assessment of accuracy and analysis of research results are given. Fields of applying hand-held laser scanners, their advantages and disadvantages are identified.

scholarly journals Automatic Reconstruction of Multi-Level Indoor Spaces from Point Cloud and Trajectory

Sensors ◽  
2021 ◽  
Vol 21 (10) ◽  
pp. 3493
Author(s):  
Gahyeon Lim ◽  
Nakju Doh
Keyword(s):  
Point Cloud ◽  
Dimensional Space ◽  
Three Dimensional ◽  
Reconstruction Method ◽  
Complex Environments ◽  
Indoor Space ◽  
Multi Level ◽  
Level Building ◽  
Indoor Spaces ◽  
Data Term

Remarkable progress in the development of modeling methods for indoor spaces has been made in recent years with a focus on the reconstruction of complex environments, such as multi-room and multi-level buildings. Existing methods represent indoor structure models as a combination of several sub-spaces, which are constructed by room segmentation or horizontal slicing approach that divide the multi-room or multi-level building environments into several segments. In this study, we propose an automatic reconstruction method of multi-level indoor spaces with unique models, including inter-room and inter-floor connections from point cloud and trajectory. We construct structural points from registered point cloud and extract piece-wise planar segments from the structural points. Then, a three-dimensional space decomposition is conducted and water-tight meshes are generated with energy minimization using graph cut algorithm. The data term of the energy function is expressed as a difference in visibility between each decomposed space and trajectory. The proposed method allows modeling of indoor spaces in complex environments, such as multi-room, room-less, and multi-level buildings. The performance of the proposed approach is evaluated for seven indoor space datasets.

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