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 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.

Continuous plane detection in point-cloud data based on 3D Hough Transform

2014 ◽  
Vol 25 (1) ◽  
pp. 86-97 ◽  
Author(s):  
Rostislav Hulik ◽  
Michal Spanel ◽  
Pavel Smrz ◽  
Zdenek Materna
Keyword(s):  
Hough Transform ◽  
Point Cloud ◽  
Point Cloud Data ◽  
Cloud Data ◽  
3D Hough Transform

scholarly journals INTERIOR RECONSTRUCTION USING THE 3D HOUGH TRANSFORM

2013 ◽  
Vol XL-5/W1 ◽  
pp. 65-72 ◽  
Author(s):  
R.-C. Dumitru ◽  
D. Borrmann ◽  
A. Nüchter
Keyword(s):  
Hough Transform ◽  
3D Hough Transform

scholarly journals Segmentation of stereo images with the use of the 3d hough transform

2016 ◽  
Author(s):  
Yegor V. Goshin ◽  
◽  
Galina E. Loshkareva ◽  
◽  
◽  
...  
Keyword(s):  
Hough Transform ◽  
Stereo Images ◽  
3D Hough Transform

scholarly journals Very High Density Point Clouds from UAV Laser Scanning for Automatic Tree Stem Detection and Direct Diameter Measurement

2020 ◽  
Vol 12 (8) ◽  
pp. 1236 ◽  
Author(s):  
Karel Kuželka ◽  
Martin Slavík ◽  
Peter Surový
Keyword(s):  
Cross Sections ◽  
Hough Transform ◽  
Laser Scanning ◽  
Point Clouds ◽  
Mean Bias Error ◽  
Bias Error ◽  
Least Trimmed Squares ◽  
Remotely Sensed Data ◽  
Individual Tree ◽  
Tree Stem

Three-dimensional light detection and ranging (LiDAR) point clouds acquired from unmanned aerial vehicles (UAVs) represent a relatively new type of remotely sensed data. Point cloud density of thousands of points per square meter with survey-grade accuracy makes the UAV laser scanning (ULS) a very suitable tool for detailed mapping of forest environment. We used RIEGL VUX-SYS to scan forest stands of Norway spruce and Scots pine, the two most important economic species of central European forests, and evaluated the suitability of point clouds for individual tree stem detection and stem diameter estimation in a fully automated workflow. We segmented tree stems based on point densities in voxels in subcanopy space and applied three methods of robust circle fitting to fit cross-sections along the stems: (1) Hough transform; (2) random sample consensus (RANSAC); and (3) robust least trimmed squares (RLTS). We detected correctly 99% and 100% of all trees in research plots for spruce and pine, respectively, and were able to estimate diameters for 99% of spruces and 98% of pines with mean bias error of −0.1 cm (−1%) and RMSE of 6.0 cm (19%), using the best performing method, RTLS. Hough transform was not able to fit perimeters in unfiltered and often incomplete point representations of cross-sections. In general, RLTS performed slightly better than RANSAC, having both higher stem detection success rate and lower error in diameter estimation. Better performance of RLTS was more pronounced in complicated situations, such as incomplete and noisy point structures, while for high-quality point representations, RANSAC provided slightly better results.

Fast 3D Hough Transform Computation

2016 ◽  
Author(s):  
Egor. I. Ershov ◽  
Arseniy P. Terekhin ◽  
Simon M. Karpenko ◽  
Dmitry P. Nikolaev ◽  
Vassili V. Postnikov
Keyword(s):  
Hough Transform ◽  
3D Hough Transform
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