Boundary constrained voxel segmentation for 3D point clouds using local geometric differences

Sağlam, Ali; Makineci, Hasan Bilgehan; Baykan, Nurdan Akhan; Baykan, Ömer Kaan

doi:10.1016/j.eswa.2020.113439

Cited by 32 publications

(24 citation statements)

References 40 publications

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“…Although the normals of two surfaces are parallel to each other, they can indicate the opposite directions. In order to rotate the normals onto the same aspect, an origin point and the barycenters of the voxels can be used [3,20,23,25,31]. If the angular difference between the unit normal vector of a voxel and the vector from the origin to the barycenter of the points in the voxel is higher than the angle 90°, the normal of the voxel is inverted, or vice versa.…”

Section: Surface Normals Of the Patchesmentioning

confidence: 99%

“…Xu et al [19] improved edge weight measurements composed with the surface normals, the vector between the barycenters, and the spatial distances of the voxels in their algorithm "Voxel and Graph-based Segmentation" (VGS). Saglam et al [20] proposed a voxelbased segmentation method that uses only the normal and barycenter features of the voxels in their algorithm "Boundary Constrained Voxel Segmentation" (BCVS).…”

Section: Introductionmentioning

confidence: 99%

“…In this paper, the BCVS algorithm is used to test the proposed refitting method as the test segmentation method because of its success and speed of operation and needing only one parameter in addition to the parameter of voxel size [20]. In Section 2, the surface normals of the patches and the BCVS segmentation method are mentioned as preliminaries.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Clustering-Based Plane Refitting of Non-planar Patches for Voxel-Based 3D Point Cloud Segmentation Using K-Means Clustering

Sağlam¹,

Makineci²,

Baykan³

et al. 2020

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Point cloud processing is a struggled field because the points in the clouds are three-dimensional and irregular distributed signals. For this reason, the points in the point clouds are mostly sampled into regularly distributed voxels in the literature. Voxelization as a pretreatment significantly accelerates the process of segmenting surfaces. The geometric cues such as plane directions (normals) in the voxels are mostly used to segment the local surfaces. However, the sampling process may include a non-planar point group (patch), which is mostly on the edges and corners, in a voxel. These voxels can cause misleading the segmentation process. In this paper, we separate the non-planar patches into planar sub-patches using k-means clustering. The largest one among the planar sub-patches replaces the normal and barycenter properties of the voxel with those of itself. We have tested this process in a successful point cloud segmentation method and measure the effects of the proposed method on two point cloud segmentation datasets (Mosque and Train Station). The method increases the accuracy success of the Mosque dataset from 83.84% to 87.86% and that of the Train Station dataset from 85.36% to 87.07%.

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Section: Surface Normals Of the Patchesmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Clustering-Based Plane Refitting of Non-planar Patches for Voxel-Based 3D Point Cloud Segmentation Using K-Means Clustering

Sağlam¹,

Makineci²,

Baykan³

et al. 2020

Self Cite

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show abstract

“…As quantitative metrics to compare the segmentation successes of weight measurements, the accuracy and F1 score measurements are used. A larger segment has more impact on the accuracy score, while each segment has the same impact on the F1 score [14]. In both metrics, the result segments are firstly matched one-to-one with their mostly overlapping segments among the reference segments.…”

Section: Evaluation Metricsmentioning

confidence: 99%

“…The deficiency of this method is that its segmentation parameter is not a certain range and it changes according to both the connection weights and sizes of segments. A novel graph-based point cloud segmentation method, which named as "Boundary Constrained Voxel Segmentation" (BCVS) in this work, is proposed by Saglam et al (2020) in the study [14]. They voxelize the points at first and merge the voxels by evaluating the weight values of connections between adjacent voxels.…”

Section: Introductionmentioning

confidence: 99%

Dissimilarity weighting for graph-based point cloud segmentation using local surface gradients

Sağlam

Makineci

Baykan

et al. 2020

International Journal of Applied Mathematics Electronics and Computers

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Processing of 3D point cloud data is seen as a problem due to the difficulties of processing millions of unstructured points. The point cloud segmentation process is a crucial pre-classification stage such that it reduces the high processing time required to extract meaningful information from raw data and produces some distinctive features for the classification stage. Local surface inclinations of objects are the most effective features of 3D point clouds to provide meaningful information about the objects. Sampling the points into sub-volumes (voxels) is a technique commonly used in the literature to obtain the required neighboring point groups to calculate local surface directions (with normal vectors). The graph-based segmentation approaches are widely used for the surface segmentation using the attributes of the local surface orientations and continuities. In this study, only two geometrical primitives which are normal vectors and barycenters of point groups are used to weight the connections between the adjacent voxels (vertices). The defined 14 possible dissimilarity calculations of three angular values getting from the primitives are experimented and evaluated on five sample datasets that have reference data for segmentation. Finally, the results of the measures are compared in terms of accuracy and F1 score. According to the results, the weight measure W7 (seventh calculation) gives 0.8026 accuracy and 0.7305 F1 score with higher standard deviations, while the original weight measure (W8) of the segmentation method gives 0.7890 accuracy and 0.6774 F1 score with lower standard deviations.

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Segmentation-Based 3D Point Cloud Classification on a Large-Scale and Indoor Semantic Segmentation Dataset

Sağlam

Baykan

2021

Innovations in Smart Cities Applications Volume 4

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Boundary constrained voxel segmentation for 3D point clouds using local geometric differences

Cited by 32 publications

References 40 publications

Clustering-Based Plane Refitting of Non-planar Patches for Voxel-Based 3D Point Cloud Segmentation Using K-Means Clustering

Clustering-Based Plane Refitting of Non-planar Patches for Voxel-Based 3D Point Cloud Segmentation Using K-Means Clustering

Dissimilarity weighting for graph-based point cloud segmentation using local surface gradients

Segmentation-Based 3D Point Cloud Classification on a Large-Scale and Indoor Semantic Segmentation Dataset

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