Facet Segmentation-Based Line Segment Extraction for Large-Scale Point Clouds

Lin, Yangbin; Wang, Cheng; Chen, Bili; Zai, Dawei; Li, Jonathan

doi:10.1109/tgrs.2016.2639025

Cited by 82 publications

(52 citation statements)

References 44 publications

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“…It requires many different techniques and has many variants, depending on the tasks and input data. A very common technique is region growing, often employed by more sophisticated algorithms to extract initial planes [10,27,29,22]. Another popular approach is RANSAC [16] and variants (e.g.…”

Section: Related Workmentioning

confidence: 99%

Fast regularity-constrained plane fitting

Lin

Wang

et al. 2020

ISPRS Journal of Photogrammetry and Remote Sensing

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Section: Related Workmentioning

confidence: 99%

Fast regularity-constrained plane fitting

Lin

Wang

et al. 2020

ISPRS Journal of Photogrammetry and Remote Sensing

Self Cite

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“…To evaluate the performance of the proposed method, the evaluation metrics of precision, recall, F1 and execution times [31,[48][49][50][51] are used to quantitatively evaluate the results of rock surface extraction at the point level. Precision represents the percentage of correctly detected elements and recall indicates the percentage of reference ground truth data that are correctly detected.…”

Section: Evaluation Metricsmentioning

confidence: 99%

Major Orientation Estimation-Based Rock Surface Extraction for 3D Rock-Mass Point Clouds

2019

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In the fields of 3D modeling, analysis of discontinuities and engineering calculation, surface extraction is of great importance. The rapid development of photogrammetry and Light Detection and Ranging (LiDAR) technology facilitates the study of surface extraction. Automatic extraction of rock surfaces from 3D rock-mass point clouds also becomes the basis of 3D modeling and engineering calculation of rock mass. This paper presents an automated and effective method for extracting rock surfaces from unorganized rock-mass point clouds. This method consists of three stages: (i) clustering based on voxels; (ii) estimating major orientations based on Gaussian Kernel and (iii) rock surface extraction. Firstly, the two-level spatial grid is used for fast voxelization and segmenting the point cloud into three types of voxels, including coplanar, non-coplanar and sparse voxels. Secondly, the coplanar voxels, rather than the scattered points, are employed to estimate major orientations by using a bivariate Gaussian Kernel. Finally, the seed voxels are selected on the basis of major orientations and the region growing method based on voxels is applied to extract rock surfaces, resulting in sets of surface clusters. The sub-surfaces of each cluster are coplanar or parallel. In this paper, artificial icosahedron point cloud and natural rock-mass point clouds are used for testing the proposed method, respectively. The experimental results show that, the proposed method can effectively and accurately extract rock surfaces in unorganized rock-mass point clouds.

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“…Therefore, it poses a great challenge to leaf segmentation methods, and sometimes even a human is uncapable of carrying out fully correct segmentations. Similar to the segmentation method based on super-pixels in 2D image processing [34], the facet over-segmentation algorithms for point clouds [4], [13] are able to segment a point cloud into flat clusters in each of which points have similar spatial characteristics. Since the facet over-segmentation works on a larger scale, it not only reduces the number of features but also avoids direct segmentation based on individual point features that are vulnerable to noise.…”

Section: Facet Over-segmentation For Filtered Point Cloudsmentioning

confidence: 99%

“…The value k is the size of the point set which is used to calculate the principal component vector by PCA. Following the suggestion in [13], the value of k is fixed to 20 for all point clouds processed in this paper. Parameter 1 d is a threshold on distance for the 3D region growing on the leaf center pre-segmentation stage.…”

Section: Parameter Tuningmentioning

confidence: 99%

An Overlapping-Free Leaf Segmentation Method for Plant Point Clouds

Cao

Shi

et al. 2019

IEEE Access

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Automatic leaf segmentation, as well as identification and classification methods that built upon it, are able to provide immediate monitoring for plant growth status to guarantee the output. Although 3D plant point clouds contain abundant phenotypic features, plant leaves are usually distributed in clusters and are sometimes seriously overlapped in the canopy. Therefore, it is still a big challenge to automatically segment each individual leaf from a highly crowded plant canopy in 3D for plant phenotyping purposes. In this work, we propose an overlapping-free individual leaf segmentation method for plant point clouds using the 3D filtering and facet region growing. In order to separate leaves with different overlapping situations, we develop a new 3D joint filtering operator, which integrates a Radius-based Outlier Filter (RBOF) and a Surface Boundary Filter (SBF) to help to separate occluded leaves. By introducing the facet over-segmentation and facet-based region growing, the noise in segmentation is suppressed and labeled leaf centers can expand to their whole leaves, respectively. Our method can work on point clouds generated from three types of 3D imaging platforms, and also suitable for different kinds of plant species. In experiments, it obtains a point-level cover rate of 97% for Epipremnum aureum, 99% for Monstera deliciosa, 99% for Calathea makoyana, and 87% for Hedera nepalensis sample plants. At the leaf level, our method reaches an average Recall at 100.00%, a Precision at 99.33%, and an average F-measure at 99.66%, respectively. The proposed method can also facilitate the automatic traits estimation of each single leaf (such as the leaf area, length, and width), which has potential to become a highly effective tool for plant research and agricultural engineering.

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Facet Segmentation-Based Line Segment Extraction for Large-Scale Point Clouds

Cited by 82 publications

References 44 publications

Fast regularity-constrained plane fitting

Fast regularity-constrained plane fitting

Major Orientation Estimation-Based Rock Surface Extraction for 3D Rock-Mass Point Clouds

An Overlapping-Free Leaf Segmentation Method for Plant Point Clouds

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