Octree-based segmentation for terrestrial LiDAR point cloud data in industrial applications

Su, Yun-Ting; Bethel, James; Hu, Shuowen

doi:10.1016/j.isprsjprs.2016.01.001

Cited by 64 publications

(42 citation statements)

References 21 publications

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“…An octree is a data structure represented by a tree, in which each branch contains eight nodes, and therefore is commonly used to partition 3-D space into voxels (Truong-Hong and Laefer, 2014;Su et al, 2016). In this work, the whole 3-D points are equally and recursively partitioned into eight voxels until reaching a predefined sub-division depth or all voxels containing less than the predefined point number.…”

Section: Octree-based Voxelizationmentioning

confidence: 99%

“…In this work, the whole 3-D points are equally and recursively partitioned into eight voxels until reaching a predefined sub-division depth or all voxels containing less than the predefined point number. Note that the octree has been used for the storage, segmentation, and compression of huge 3-D point clouds (Wang and Tseng, 2011;Elseberg et al, 2013;Su et al, 2016), as well as visualization (Wurm et al, 2010). The input 3-D points are recursively partitioned into voxels with a given voxel size, vp.…”

Section: Octree-based Voxelizationmentioning

confidence: 99%

See 1 more Smart Citation

A Voxel-Based Filtering Algorithm for Mobile Lidar Data

Qin

Guan

et al. 2018

Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci.

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Abstract:This paper presents a stepwise voxel-based filtering algorithm for mobile LiDAR data. In the first step, to improve computational efficiency, mobile LiDAR points, in xy-plane, are first partitioned into a set of two-dimensional (2-D) blocks with a given block size, in each of which all laser points are further organized into an octree partition structure with a set of three-dimensional (3-D) voxels. Then, a voxel-based upward growing processing is performed to roughly separate terrain from non-terrain points with global and local terrain thresholds. In the second step, the extracted terrain points are refined by computing voxel curvatures. This voxel-based filtering algorithm is comprehensively discussed in the analyses of parameter sensitivity and overall performance. An experimental study performed on multiple point cloud samples, collected by different commercial mobile LiDAR systems, showed that the proposed algorithm provides a promising solution to terrain point extraction from mobile point clouds.

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Section: Octree-based Voxelizationmentioning

confidence: 99%

Section: Octree-based Voxelizationmentioning

confidence: 99%

A Voxel-Based Filtering Algorithm for Mobile Lidar Data

Qin

Guan

et al. 2018

Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci.

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“…Theoretically, region growing-based methods can keep the boundaries of surfaces well, but they are sensitive to noise and outliers. For example, over-segmentation can easily occur for large curvature objects (e.g., pipes with a long radius elbow joint) although the surfaces of which are smoothly connected (Su et al, 2016). On the other hand, their performances largely resort to the selection of seeds (e.g., the location and distribution of seeds).…”

Section: Related Workmentioning

confidence: 99%

“…In Vo et al, (2015), the octree structure and the region growing process are combined for the fast surface patch segmentation. Whereas, the octree-based voxel structure combined with graphbased sub -splitting is applied to segment cylindrical objects in industrial scenes (Su et al, 2016). Using voxel structure apparently reduces the computation cost and suppress negative effects of outliers and varying point densities.…”

Section: Related Workmentioning

confidence: 99%

Voxel- And Graph-Based Point Cloud Segmentation of 3d Scenes Using Perceptual Grouping Laws

Hoegner

Tuttas

et al. 2017

ISPRS Ann. Photogramm. Remote Sens. Spatial Inf. Sci.

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ABSTRACT:Segmentation is the fundamental step for recognizing and extracting objects from point clouds of 3D scene. In this paper, we present a strategy for point cloud segmentation using voxel structure and graph-based clustering with perceptual grouping laws, which allows a learning-free and completely automatic but parametric solution for segmenting 3D point cloud. To speak precisely, two segmentation methods utilizing voxel and supervoxel structures are reported and tested. The voxel-based data structure can increase efficiency and robustness of the segmentation process, suppressing the negative effect of noise, outliers, and uneven points densities. The clustering of voxels and supervoxel is carried out using graph theory on the basis of the local contextual information, which commonly conducted utilizing merely pairwise information in conventional clustering algorithms. By the use of perceptual laws, our method conducts the segmentation in a pure geometric way avoiding the use of RGB color and intensity information, so that it can be applied to more general applications. Experiments using different datasets have demonstrated that our proposed methods can achieve good results, especially for complex scenes and nonplanar surfaces of objects. Quantitative comparisons between our methods and other representative segmentation methods also confirms the effectiveness and efficiency of our proposals.

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“…Cylindrical features are one of the most encountered geometric primitives in point clouds when collected in industrial sites, urban areas, street corridors, but also forest. Cylinder fitting is a fundamental task in computer graphics, computer vision, image analysis, reverse engineering, pattern recognition, photogrammetry and remote sensing for various applications like feature extraction (De Guevara et al, 2011), surface reconstruction (Schnabel et al, 2007), archaeological documentation, street furniture management, building information modelling (Kwon et al, 2004), as-built modelling (Ahmed et al, 2014), machine tools quality control, industrial plant settings (Su et al, 2016), tunnel monitoring (Gosliga et al, 2006), and forest inventory (Lalonde et al, 2006), and therefore has been studied rigorously.…”

Section: Introductionmentioning

confidence: 99%

Robust Cylinder Fitting in Three-Dimensional Point Cloud Data

Nurunnabi

Sadahiro

Lindenbergh

2017

Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci.

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ABSTRACT:This paper investigates the problems of cylinder fitting in laser scanning three-dimensional Point Cloud Data (PCD). Most existing methods require full cylinder data, do not study the presence of outliers, and are not statistically robust. But especially mobile laser scanning often has incomplete data, as street poles for example are only scanned from the road. Moreover, existence of outliers is common. Outliers may occur as random or systematic errors, and may be scattered and/or clustered. In this paper, we present a statistically robust cylinder fitting algorithm for PCD that combines Robust Principal Component Analysis (RPCA) with robust regression. Robust principal components as obtained by RPCA allow estimating cylinder directions more accurately, and an existing efficient circle fitting algorithm following robust regression principles, properly fit cylinder. We demonstrate the performance of the proposed method on artificial and real PCD. Results show that the proposed method provides more accurate and robust results: (i) in the presence of noise and high percentage of outliers, (ii) for incomplete as well as complete data, (iii) for small and large number of points, and (iv) for different sizes of radius. On 1000 simulated quarter cylinders of 1m radius with 10% outliers a PCA based method fit cylinders with a radius of on average 3.63meter (m); the proposed method on the other hand fit cylinders of on average 1.02 m radius. The algorithm has potential in applications such as fitting cylindrical (e.g., light and traffic) poles, diameter at breast height estimation for trees, and building and bridge information modelling.

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Octree-based segmentation for terrestrial LiDAR point cloud data in industrial applications

Cited by 64 publications

References 21 publications

A Voxel-Based Filtering Algorithm for Mobile Lidar Data

A Voxel-Based Filtering Algorithm for Mobile Lidar Data

Voxel- And Graph-Based Point Cloud Segmentation of 3d Scenes Using Perceptual Grouping Laws

Robust Cylinder Fitting in Three-Dimensional Point Cloud Data

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