An overview of constructing geometric models of buildings using point clouds

Hemi-form Geometric Models for Single-scan 3D Point Clouds

Journal of the Robotics Society of Japan ◽

10.7210/jrsj.32.721 ◽

2014 ◽

Vol 32 (8) ◽

pp. 721-730

Author(s):

Eiichi Horiuchi

Keyword(s):

Point Clouds ◽

Geometric Models ◽

3D Point Clouds

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An Efficient Approach to Automatic Construction of 3D Watertight Geometry of Buildings Using Point Clouds

Remote Sensing ◽

10.3390/rs13101947 ◽

2021 ◽

Vol 13 (10) ◽

pp. 1947

Author(s):

Yuanzhi Cai ◽

Lei Fan

Keyword(s):

Point Cloud ◽

Geometric Model ◽

Computation Time ◽

Point Clouds ◽

3D Models ◽

Ray Casting ◽

Point Cloud Data ◽

Geometric Models ◽

Cloud Data ◽

Reconstruction Methods

Recent years have witnessed an increasing use of 3D models in general and 3D geometric models specifically of built environment for various applications, owing to the advancement of mapping techniques for accurate 3D information. Depending on the application scenarios, there exist various types of approaches to automate the construction of 3D building geometry. However, in those studies, less attention has been paid to watertight geometries derived from point cloud data, which are of use to the management and the simulations of building energy. To this end, an efficient reconstruction approach was introduced in this study and involves the following key steps. The point cloud data are first voxelised for the ray-casting analysis to obtain the 3D indoor space. By projecting it onto a horizontal plane, an image representing the indoor area is obtained and is used for the room segmentation. The 2D boundary of each room candidate is extracted using new grammar rules and is extruded using the room height to generate 3D models of individual room candidates. The room connection analyses are applied to the individual models obtained to determine the locations of doors and the topological relations between adjacent room candidates for forming an integrated and watertight geometric model. The approach proposed was tested using the point cloud data representing six building sites of distinct spatial confirmations of rooms, corridors and openings. The experimental results showed that accurate watertight building geometries were successfully created. The average differences between the point cloud data and the geometric models obtained were found to range from 12 to 21 mm. The maximum computation time taken was less than 5 min for the point cloud of approximately 469 million data points, more efficient than the typical reconstruction methods in the literature.

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A Taxonomy for Depicting Geospatial Deviations of Facilities Extracted through Comparisons between Point Clouds and Building Information Models

Computing in Civil Engineering (2012) ◽

10.1061/9780784412343.0062 ◽

2012 ◽

Cited By ~ 2

Author(s):

M. Eybpoosh ◽

B. Akinci ◽

M. Bergés

Keyword(s):

Point Clouds ◽

Information Models ◽

Building Information ◽

Building Information Models

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ALGEBRA OF PREDICATES AND RELATED GEOMETRIC MODELS CREATION IN REGARDS OF UNIFIED STATE EXAM IN INFORMATICS (RUSSIAN EGE)

Informatics in school ◽

10.32517/2221-1993-2019-18-3-40-47 ◽

2019 ◽

pp. 40-47

Author(s):

E. A. Mironchik

Keyword(s):

Geometric Model ◽

Main Idea ◽

Formal Logic ◽

High Complexity ◽

Geometric Models ◽

Complexity Problem ◽

Logical Expression ◽

State Examination

The article discusses the method of solving the task 18 on the Unified State Examination in Informatics (Russian EGE). The main idea of the method is to write the conditions of the problem utilizing the language of formal logic, using elementary predicates. According to the laws of logic the resulting complex logical expression would be transformed into an expression, according to which a geometric model is supposed to be constructed which allows to obtain an answer. The described algorithm does allow high complexity problem to be converted into a simple one.

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Registration and Fusion of UAV LiDAR System Sequence Images and Laser Point Clouds

Journal of Imaging Science and Technology ◽

10.2352/j.imagingsci.technol.2021.65.1.01050110.2352/j.imagingsci.technol.2021.65.1.010501 ◽

2020 ◽

Author(s):

Jiayong Yu ◽

Longchen Ma ◽

Maoyi Tian, ◽

Xiushan Lu

Keyword(s):

Point Cloud ◽

Image Data ◽

Point Clouds ◽

Optical Image ◽

Point Cloud Data ◽

Feature Points ◽

Lidar System ◽

Registration Accuracy ◽

Cloud Data ◽

Intensity Image

The unmanned aerial vehicle (UAV)-mounted mobile LiDAR system (ULS) is widely used for geomatics owing to its efficient data acquisition and convenient operation. However, due to limited carrying capacity of a UAV, sensors integrated in the ULS should be small and lightweight, which results in decrease in the density of the collected scanning points. This affects registration between image data and point cloud data. To address this issue, the authors propose a method for registering and fusing ULS sequence images and laser point clouds, wherein they convert the problem of registering point cloud data and image data into a problem of matching feature points between the two images. First, a point cloud is selected to produce an intensity image. Subsequently, the corresponding feature points of the intensity image and the optical image are matched, and exterior orientation parameters are solved using a collinear equation based on image position and orientation. Finally, the sequence images are fused with the laser point cloud, based on the Global Navigation Satellite System (GNSS) time index of the optical image, to generate a true color point cloud. The experimental results show the higher registration accuracy and fusion speed of the proposed method, thereby demonstrating its accuracy and effectiveness.

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