scholarly journals A Novel Indoor Structure Extraction Based on Dense Point Cloud

2020 ◽  
Vol 9 (11) ◽  
pp. 660
Author(s):  
Pengcheng Shi ◽  
Qin Ye ◽  
Lingwen Zeng
Keyword(s):  
Point Cloud ◽  
Ground Plane ◽  
Region Growing ◽  
Feature Representation ◽  
Planar Structure ◽  
Planar Point ◽  
Segmentation Approach ◽  
Public Datasets ◽  
Planar Feature ◽  
Structure Extraction

Herein, we propose a novel indoor structure extraction (ISE) method that can reconstruct an indoor planar structure with a feature structure map (FSM) and enable indoor robot navigation using a navigation structure map (NSM). To construct the FSM, we first propose a two-staged region growing algorithm to segment the planar feature and to obtain the original planar point cloud. Subsequently, we simplify the planar feature using quadtree segmentation based on cluster fusion. Finally, we perform simple triangulation in the interior and vertex-assignment triangulation in the boundary to accomplish feature reconstruction for the planar structure. The FSM is organized in the form of a mesh model. To construct the NSM, we first propose a novel ground extraction method based on indoor structure analysis under the Manhattan world assumption. It can accurately capture the ground plane in an indoor scene. Subsequently, we establish a passable area map (PAM) within different heights. Finally, a novel-form NSM is established using the original planar point cloud and the PAM. Experiments are performed using three public datasets and one self-collected dataset. The proposed plane segmentation approach is evaluated on two simulation datasets and achieves a recall of approximately 99%, which is 5% higher than that of the traditional plane segmentation method. Furthermore, the triangulation performance of our method compared with the traditional greedy projection triangulation show that our method performs better in terms of feature representation. The experimental results reveal that our ISE method is robust and effective for extracting indoor structures.

Line-Based Planar Structure Extraction from a Point Cloud with an Anisotropic Distribution

2017 ◽  
Vol 11 (4) ◽  
pp. 657-665 ◽  
Author(s):  
Ryuji Miyazaki ◽  
Makoto Yamamoto ◽  
Koichi Harada ◽  
◽  
◽  
...  
Keyword(s):  
Point Cloud ◽  
Laser Scanning ◽  
Region Growing ◽  
Point Clouds ◽  
Planar Structure ◽  
Scanning System ◽  
Normal Vector ◽  
Real Point ◽  
Planar Regions ◽  
Structure Extraction

We propose a line-based region growing method for extracting planar regions with precise boundaries from a point cloud with an anisotropic distribution. Planar structure extraction from point clouds is an important process in many applications, such as maintenance of infrastructure components including roads and curbstones, because most artificial structures consist of planar surfaces. A mobile mapping system (MMS) is able to obtain a large number of points while traveling at a standard speed. However, if a high-end laser scanning system is equipped, the point cloud has an anisotropic distribution. In traditional point-based methods, this causes problems when calculating geometric information using neighboring points. In the proposed method, the precise boundary of a planar structure is maintained by appropriately creating line segments from an input point cloud. Furthermore, a normal vector at a line segment is precisely estimated for the region growing process. An experiment using the point cloud from an MMS simulation indicates that the proposed method extracts planar regions accurately. Additionally, we apply the proposed method to several real point clouds and evaluate its effectiveness via visual inspection.

scholarly journals Leaf Segmentation on Dense Plant Point Clouds with Facet Region Growing

Sensors ◽  
2018 ◽  
Vol 18 (11) ◽  
pp. 3625 ◽  
Author(s):  
Dawei Li ◽  
Yan Cao ◽  
Xue-song Tang ◽  
Siyuan Yan ◽  
Xin Cai
Keyword(s):  
Point Cloud ◽  
Active Material ◽  
Region Growing ◽  
Point Clouds ◽  
Phenotypic Traits ◽  
Individual Leaf ◽  
3D Point Clouds ◽  
Segmentation Approach ◽  
Monstera Deliciosa ◽  
Leaf Segmentation

Leaves account for the largest proportion of all organ areas for most kinds of plants, and are comprise the main part of the photosynthetically active material in a plant. Observation of individual leaves can help to recognize their growth status and measure complex phenotypic traits. Current image-based leaf segmentation methods have problems due to highly restricted species and vulnerability toward canopy occlusion. In this work, we propose an individual leaf segmentation approach for dense plant point clouds using facet over-segmentation and facet region growing. The approach can be divided into three steps: (1) point cloud pre-processing, (2) facet over-segmentation, and (3) facet region growing for individual leaf segmentation. The experimental results show that the proposed method is effective and efficient in segmenting individual leaves from 3D point clouds of greenhouse ornamentals such as Epipremnum aureum, Monstera deliciosa, and Calathea makoyana, and the average precision and recall are both above 90%. The results also reveal the wide applicability of the proposed methodology for point clouds scanned from different kinds of 3D imaging systems, such as stereo vision and Kinect v2. Moreover, our method is potentially applicable in a broad range of applications that aim at segmenting regular surfaces and objects from a point cloud.

An Efficient Planar Feature Fitting Method Using Point Cloud Simplification and Threshold-Independent BaySAC

2016 ◽  
Vol 13 (12) ◽  
pp. 1842-1846 ◽  
Author(s):  
Zhizhong Kang ◽  
Ruofei Zhong ◽  
Ai Wu ◽  
Zhenwei Shi ◽  
Zhongfei Luo
Keyword(s):  
Point Cloud ◽  
Fitting Method ◽  
Feature Fitting ◽  
Point Cloud Simplification ◽  
Planar Feature

scholarly journals AUTOMATED DETECTION AND CLOSING OF HOLES IN AERIAL POINT CLOUDS USING AN UAS

2017 ◽  
Vol XLII-2/W6 ◽  
pp. 101-107
Author(s):  
T. Fiolka ◽  
F. Rouatbi ◽  
D. Bender
Keyword(s):  
Point Cloud ◽  
Ground Plane ◽  
Point Clouds ◽  
Current Approach ◽  
Automated Detection ◽  
Lidar System ◽  
3D Point Clouds ◽  
Terrain Models ◽  
3D Terrain ◽  
Flight Route

3D terrain models are an important instrument in areas like geology, agriculture and reconnaissance. Using an automated UAS with a line-based LiDAR can create terrain models fast and easily even from large areas. But the resulting point cloud may contain holes and therefore be incomplete. This might happen due to occlusions, a missed flight route due to wind or simply as a result of changes in the ground height which would alter the swath of the LiDAR system. This paper proposes a method to detect holes in 3D point clouds generated during the flight and adjust the course in order to close them. First, a grid-based search for holes in the horizontal ground plane is performed. Then a check for vertical holes mainly created by buildings walls is done. Due to occlusions and steep LiDAR angles, closing the vertical gaps may be difficult or even impossible. Therefore, the current approach deals with holes in the ground plane and only marks the vertical holes in such a way that the operator can decide on further actions regarding them. The aim is to efficiently create point clouds which can be used for the generation of complete 3D terrain models.

Multi-Factor Segmentation of Point Cloud Based on Improved Multi-Rule Region Growing

2021 ◽  
Vol 41 (5) ◽  
pp. 0528001
Author(s):  
汪文琪 Wang Wenqi ◽  
李宗春 Li Zongchun ◽  
付永健 Fu Yongjian ◽  
何华 He Hua ◽  
熊峰 Xiong Feng
Keyword(s):  
Point Cloud ◽  
Region Growing

scholarly journals A Novel Point Cloud Encoding Method Based on Local Information for 3D Classification and Segmentation

Sensors ◽  
2020 ◽  
Vol 20 (9) ◽  
pp. 2501 ◽  
Author(s):  
Yanan Song ◽  
Liang Gao ◽  
Xinyu Li ◽  
Weiming Shen
Keyword(s):  
Deep Learning ◽  
Point Cloud ◽  
Semantic Segmentation ◽  
Local Information ◽  
Local Region ◽  
Feature Representation ◽  
Learning Network ◽  
Feature Representations ◽  
Encoding Method ◽  
Deep Learning Network

Deep learning is robust to the perturbation of a point cloud, which is an important data form in the Internet of Things. However, it cannot effectively capture the local information of the point cloud and recognize the fine-grained features of an object. Different levels of features in the deep learning network are integrated to obtain local information, but this strategy increases network complexity. This paper proposes an effective point cloud encoding method that facilitates the deep learning network to utilize the local information. An axis-aligned cube is used to search for a local region that represents the local information. All of the points in the local region are available to construct the feature representation of each point. These feature representations are then input to a deep learning network. Two well-known datasets, ModelNet40 shape classification benchmark and Stanford 3D Indoor Semantics Dataset, are used to test the performance of the proposed method. Compared with other methods with complicated structures, the proposed method with only a simple deep learning network, can achieve a higher accuracy in 3D object classification and semantic segmentation.

scholarly journals An Efficient Encoding Voxel-Based Segmentation (EVBS) Algorithm Based on Fast Adjacent Voxel Search for Point Cloud Plane Segmentation

2019 ◽  
Vol 11 (23) ◽  
pp. 2727 ◽  
Author(s):  
Ming Huang ◽  
Pengcheng Wei ◽  
Xianglei Liu
Keyword(s):  
Point Cloud ◽  
Initial Point ◽  
Computation Time ◽  
Region Growing ◽  
Point Clouds ◽  
Recall Rate ◽  
Seed Selection ◽  
Cloud Processing ◽  
Point Cloud Segmentation ◽  
Noise Data

Plane segmentation is a basic yet important process in light detection and ranging (LiDAR) point cloud processing. The traditional point cloud plane segmentation algorithm is typically affected by the number of point clouds and the noise data, which results in slow segmentation efficiency and poor segmentation effect. Hence, an efficient encoding voxel-based segmentation (EVBS) algorithm based on a fast adjacent voxel search is proposed in this study. First, a binary octree algorithm is proposed to construct the voxel as the segmentation object and code the voxel, which can compute voxel features quickly and accurately. Second, a voxel-based region growing algorithm is proposed to cluster the corresponding voxel to perform the initial point cloud segmentation, which can improve the rationality of seed selection. Finally, a refining point method is proposed to solve the problem of under-segmentation in unlabeled voxels by judging the relationship between the points and the segmented plane. Experimental results demonstrate that the proposed algorithm is better than the traditional algorithm in terms of computation time, extraction accuracy, and recall rate.

scholarly journals Lung Lobe Segmentation Based on Lung Fissure Surface Classification Using a Point Cloud Region Growing Approach

Algorithms ◽  
2020 ◽  
Vol 13 (10) ◽  
pp. 263
Author(s):  
Xin Chen ◽  
Hong Zhao ◽  
Ping Zhou
Keyword(s):  
Point Cloud ◽  
Principal Component ◽  
Region Growing ◽  
Disease Diagnosis ◽  
Ct Images ◽  
Normal Vector ◽  
Lung Lobe ◽  
Surface Classification ◽  
Lobe Segmentation ◽  
Lung Lobes

In anatomy, the lung can be divided by lung fissures into several pulmonary lobe units with specific functions. Identifying the lung lobes and the distribution of various diseases among different lung lobes from CT images is important for disease diagnosis and tracking after recovery. In order to solve the problems of low tubular structure segmentation accuracy and long algorithm time in segmenting lung lobes based on lung anatomical structure information, we propose a segmentation algorithm based on lung fissure surface classification using a point cloud region growing approach. We cluster the pulmonary fissures, transformed into point cloud data, according to the differences in the pulmonary fissure surface normal vector and curvature estimated by principal component analysis. Then, a multistage spline surface fitting method is used to fill and expand the lung fissure surface to realize the lung lobe segmentation. The proposed approach was qualitatively and quantitatively evaluated on a public dataset from Lobe and Lung Analysis 2011 (LOLA11), and obtained an overall score of 0.84. Although our approach achieved a slightly lower overall score compared to the deep learning based methods (LobeNet_V2 and V-net), the inter-lobe boundaries from our approach were more accurate for the CT images with visible lung fissures.

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