scholarly journals Fast Planar Detection System Using a GPU-Based 3D Hough Transform for LiDAR Point Clouds

2020 ◽  
Vol 10 (5) ◽  
pp. 1744 ◽  
Author(s):  
Yifei Tian ◽  
Wei Song ◽  
Long Chen ◽  
Yunsick Sung ◽  
Jeonghoon Kwak ◽  
...  
Keyword(s):  
Hough Transform ◽  
Detection System ◽  
Point Clouds ◽  
Detection Algorithm ◽  
Polar Coordinates ◽  
Processing Unit ◽  
3D Point Clouds ◽  
Hough Space ◽  
Plane Extraction ◽  
3D Hough Transform

Plane extraction is regarded as a necessary function that supports judgment basis in many applications, including semantic digital map reconstruction and path planning for unmanned ground vehicles. Owing to the heterogeneous density and unstructured spatial distribution of three-dimensional (3D) point clouds collected by light detection and ranging (LiDAR), plane extraction from it is recently a significant challenge. This paper proposed a parallel 3D Hough transform algorithm to realize rapid and precise plane detection from 3D LiDAR point clouds. After transforming all the 3D points from a Cartesian coordinate system to a pre-defined 3D Hough space, the generated Hough space is rasterised into a series of arranged cells to store the resided point counts into individual cells. A 3D connected component labeling algorithm is developed to cluster the cells with high values in Hough space into several clusters. The peaks from these clusters are extracted so that the targeting planar surfaces are obtained in polar coordinates. Because the laser beams emitted by LiDAR sensor holds several fixed angles, the collected 3D point clouds distribute as several horizontal and parallel circles in plane surfaces. This kind of horizontal and parallel circles mislead plane detecting results from horizontal wall surfaces to parallel planes. For detecting accurate plane parameters, this paper adopts a fraction-to-fraction method to gradually transform raw point clouds into a series of sub Hough space buffers. In our proposed planar detection algorithm, a graphic processing unit (GPU) programming technology is applied to speed up the calculation of 3D Hough space updating and peaks searching.

Extracting Geometric Edges from 3D Point Clouds Based on Normal Vector Change

2014 ◽  
Vol 571-572 ◽  
pp. 729-734
Author(s):  
Jia Li ◽  
Huan Lin ◽  
Duo Qiang Zhang ◽  
Xiao Lu Xue
Keyword(s):  
Point Clouds ◽  
Detection Algorithm ◽  
Growth Strategy ◽  
Normal Vector ◽  
Geometric Morphometric ◽  
3D Point Clouds ◽  
Real Scene ◽  
Edge Points ◽  
Normal Vectors ◽  
Change Intensity

Normal vector of 3D surface is important differential geometric property over localized neighborhood, and its abrupt change along the surface directly reflects the variation of geometric morphometric. Based on this observation, this paper presents a novel edge detection algorithm in 3D point clouds, which utilizes the change intensity and change direction of adjacent normal vectors and is composed of three steps. First, a two-dimensional grid is constructed according to the inherent data acquisition sequence so as to build up the topology of points. Second, by this topological structure preliminary edge points are retrieved, and the potential directions of edges passing through them are estimated according to the change of normal vectors between adjacent points. Finally, an edge growth strategy is designed to regain the missing edge points and connect them into complete edge lines. The results of experiment in a real scene demonstrate that the proposed algorithm can extract geometric edges from 3D point clouds robustly, and is able to reduce edge quality’s dependence on user defined parameters.

The 3D Hough Transform for plane detection in point clouds: A review and a new accumulator design

3D Research ◽  
2011 ◽  
Vol 2 (2) ◽  
Author(s):  
Dorit Borrmann ◽  
Jan Elseberg ◽  
Kai Lingemann ◽  
Andreas Nüchter
Keyword(s):  
Hough Transform ◽  
Point Clouds ◽  
3D Hough Transform

3D Hough transform for sphere recognition on point clouds

2014 ◽  
Vol 25 (7) ◽  
pp. 1877-1891 ◽  
Author(s):  
Marco Camurri ◽  
Roberto Vezzani ◽  
Rita Cucchiara
Keyword(s):  
Hough Transform ◽  
Point Clouds ◽  
3D Hough Transform

scholarly journals Sphere Detection in Kinect Point Clouds via the 3D Hough Transform

2013 ◽  
pp. 290-297 ◽  
Author(s):  
Anas Abuzaina ◽  
Mark S. Nixon ◽  
John N. Carter
Keyword(s):  
Hough Transform ◽  
Point Clouds ◽  
Sphere Detection ◽  
3D Hough Transform

scholarly journals Automatic 2D Floorplan CAD Generation from 3D Point Clouds

2020 ◽  
Vol 10 (8) ◽  
pp. 2817 ◽  
Author(s):  
Uuganbayar Gankhuyag ◽  
Ji-Hyeong Han
Keyword(s):  
Point Clouds ◽  
Detection Algorithm ◽  
Information Modeling ◽  
Ongoing Research ◽  
Cloud Data ◽  
Industry Foundation Classes ◽  
Automated Method ◽  
3D Point Clouds ◽  
Building Information ◽  
Geometric Quality

In the architecture, engineering, and construction (AEC) industry, creating an indoor model of existing buildings has been a challenging task since the introduction of building information modeling (BIM). Because the process of BIM is primarily manual and implies a high possibility of error, the automated creation of indoor models remains an ongoing research. In this paper, we propose a fully automated method to generate 2D floorplan computer-aided designs (CADs) from 3D point clouds. The proposed method consists of two main parts. The first is to detect planes in buildings, such as walls, floors, and ceilings, from unstructured 3D point clouds and to classify them based on the Manhattan-World (MW) assumption. The second is to generate 3D BIM in the industry foundation classes (IFC) format and a 2D floorplan CAD using the proposed line-detection algorithm. We experimented the proposed method on 3D point cloud data from a university building, residential houses, and apartments and evaluated the geometric quality of a wall reconstruction. We also offer the source code for the proposed method on GitHub.

Registration of 3D Point Clouds for Ship Block Measurement

2015 ◽  
Author(s):  
Sunghan Kim ◽  
Mingyu Kim ◽  
Jeongtae Lee ◽  
Jinhwi Pyo ◽  
Heeyoung Heo ◽  
...  
Keyword(s):  
Point Cloud ◽  
Measurement Data ◽  
User Interaction ◽  
Point Clouds ◽  
Processing Unit ◽  
Feature Points ◽  
Automatic Registration ◽  
Actual Measurement ◽  
Large Size ◽  
3D Point Clouds

In this paper, a software system for registration of point clouds is developed. The system consists of two modules for registration and user interaction. The registration module contains functions for manual and automatic registration. The manual method allows a user to select feature points or planes from the point clouds manually. The selected planes or features are then processed to establish the correspondence between the point clouds, and registration is performed to obtain one large point cloud. The automatic registration uses sphere targets. Sphere targets are attached to an object of interest. A scanner measures the object as well as the targets to produce point clouds, from which the targets are extracted using shape intrinsic properties. Then correspondence between the point clouds is obtained using the targets, and the registration is performed. The user interaction module provides a GUI environment which allows a user to navigate point clouds, to compute various features, to visualize point clouds and to select/unselect points interactively and the point-processing unit containing functions for filtering, estimation of geometric features, and various data structures for managing point clouds of large size. The developed system is tested with actual measurement data of various blocks in a shipyard.

scholarly journals Detecting moving spheres in 3D point clouds via the 3D velocity Hough Transform

IVMSP 2013 ◽  
2013 ◽  
Author(s):  
Anas Abuzaina ◽  
Thamer Alathari ◽  
Mark S. Nixon ◽  
John N. Carter
Keyword(s):  
Hough Transform ◽  
Point Clouds ◽  
3D Point Clouds ◽  
Moving Spheres

scholarly journals Impact-Detection Algorithm That Uses Point Clouds as Topographic Inputs for 3D Rockfall Simulations

Geosciences ◽  
2021 ◽  
Vol 11 (5) ◽  
pp. 188
Author(s):  
François Noël ◽  
Catherine Cloutier ◽  
Michel Jaboyedoff ◽  
Jacques Locat
Keyword(s):  
Surface Roughness ◽  
Digital Terrain Model ◽  
Simulation Models ◽  
Point Clouds ◽  
Detection Algorithm ◽  
Simulation Software ◽  
Impact Detection ◽  
3D Point Clouds ◽  
Terrain Models ◽  
Rockfall Simulation

Numerous 3D rockfall simulation models use coarse gridded digital terrain model (DTM raster) as their topography input. Artificial surface roughness is often added to overcome the loss of details that occurs during the gridding process. Together with the use of sensitive energy damping parameters, they provide great freedom to the user at the expense of the objectivity of the method. To quantify and limit the range of such artificial values, we developed an impact-detection algorithm that can be used to extract the perceived surface roughness from detailed terrain samples in relation to the size of the impacting rocks. The algorithm can also be combined with a rebound model to perform rockfall simulations directly on detailed 3D point clouds. The abilities of the algorithm are demonstrated by objectively extracting different perceived surface roughnesses from detailed terrain samples and by simulating rockfalls on detailed terrain models as proof of concept. The results produced are also compared to that of rockfall simulation software CRSP 4, RocFall 8 and Rockyfor3D 5.2.15 as validation. Although differences were observed, the validation shows that the algorithm can produce similar results. With the presented approach not being limited to coarse terrain models, the need for adding artificial terrain roughness or for adjusting sensitive damping parameters on a per-site basis is reduced, thereby limiting the related biases and subjectivity.

Excretion Detection System with Gas Sensor – Proposal and Verification of Algorithm Based on Time-Series Clustering –

2017 ◽  
Vol 29 (2) ◽  
pp. 353-363 ◽  
Author(s):  
Yoshimi Ui ◽  
◽  
Yutaka Akiba ◽  
Shohei Sugano ◽  
Ryosuke Imai ◽  
...  
Keyword(s):  
Time Series ◽  
Gas Sensor ◽  
Gas Sensors ◽  
Clustering Algorithm ◽  
Time Series Data ◽  
Detection System ◽  
Detection Algorithm ◽  
Sensor Data ◽  
Series Data ◽  
Processing Unit

[abstFig src='/00290002/09.jpg' width='300' text='Standard Lifilm configuration' ] In this study, we propose an excretion detection system, Lifi, which does not require sensors inside diapers, and we verify its capabilities. It consists of a sheet with strategically placed air intakes, a set of gas sensors, and a processing unit with a newly developed excretion detection algorithm. The gas sensor detects chemicals with odor in the excrement, such as hydrogen sulfide and urea. The time-series data from the gas sensor was used for the detection of not only excretion, but also of the presence/absence of the cared person on the bed. We examined two algorithms, one with a simple threshold and another based on the clustering of sensor data, obtained using the<span class=”bold”>k</span>-means method. The results from both algorithms were satisfactory and similar, once the algorithms were customized for each cared person. However, we adopted the clustering algorithm because it possesses a higher level of flexibility that can be explored and exploited. Lifi was conceived from an overwhelming and serious desire of caretakers to discover the excretion of bed-ridden cared persons, without opening their diapers. We believe that Lifi, along with the clustering algorithm, can help caretakers in this regard.

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