CSF BASED NON-GROUND POINTS EXTRACTION FROM LIDAR DATA

Region growing is a classical method of point cloud segmentation. Based on the idea of collecting the pixels with similar properties to form regions, region growing is widely used in many fields such as medicine, forestry and remote sensing. In this algorithm, there are two core problems. One is the selection of seed points, the other is the setting of the growth constraints, in which the selection of the seed points is the foundation. In this paper, we propose a CSF (Cloth Simulation Filtering) based method to extract the non-ground seed points effectively. The experiments have shown that this method can obtain a group of seed spots compared with the traditional methods. It is a new attempt to extract seed points

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LiDAR Segmentation using Suitable Seed Points for 3D Building Extraction

ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences ◽

10.5194/isprsarchives-xl-3-1-2014 ◽

2014 ◽

Vol XL-3 ◽

pp. 1-8 ◽

Cited By ~ 6

Author(s):

S. M. Abdullah ◽

M. Awrangjeb ◽

G. Lu

Keyword(s):

Point Cloud ◽

Region Growing ◽

Data Sets ◽

Building Detection ◽

Building Extraction ◽

Seed Point ◽

Point Cloud Segmentation ◽

Ground Point ◽

Plane Extraction ◽

Seed Points

Effective building detection and roof reconstruction has an influential demand over the remote sensing research community. In this paper, we present a new automatic LiDAR point cloud segmentation method using suitable seed points for building detection and roof plane extraction. Firstly, the LiDAR point cloud is separated into "ground" and "non-ground" points based on the analysis of DEM with a height threshold. Each of the non-ground point is marked as coplanar or non-coplanar based on a coplanarity analysis. Commencing from the maximum LiDAR point height towards the minimum, all the LiDAR points on each height level are extracted and separated into several groups based on 2D distance. From each group, lines are extracted and a coplanar point which is the nearest to the midpoint of each line is considered as a seed point. This seed point and its neighbouring points are utilised to generate the plane equation. The plane is grown in a region growing fashion until no new points can be added. A robust rule-based tree removal method is applied subsequently to remove planar segments on trees. Four different rules are applied in this method. Finally, the boundary of each object is extracted from the segmented LiDAR point cloud. The method is evaluated with six different data sets consisting hilly and densely vegetated areas. The experimental results indicate that the proposed method offers a high building detection and roof plane extraction rates while compared to a recently proposed method.

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Automatic Seeded Region Growing Image Segmentation for Medical Image Segmentation: A Brief Review

International Journal of Image and Graphics ◽

10.1142/s0219467820500187 ◽

2020 ◽

Vol 20 (03) ◽

pp. 2050018

Author(s):

Neeraj Shrivastava ◽

Jyoti Bharti

Keyword(s):

Image Segmentation ◽

Region Growing ◽

Magnetic Resonance Images ◽

Medical Image Segmentation ◽

Seed Selection ◽

Seeded Region Growing ◽

Processing Strategies ◽

Edge Based ◽

Seed Points ◽

Selection Of

In the domain of computer technology, image processing strategies have become a part of various applications. A few broadly used image segmentation methods have been characterized as seeded region growing (SRG), edge-based image segmentation, fuzzy [Formula: see text]-means image segmentation, etc. SRG is a quick, strongly formed and impressive image segmentation algorithm. In this paper, we delve into different applications of SRG and their analysis. SRG delivers better results in analysis of magnetic resonance images, brain image, breast images, etc. On the other hand, it has some limitations as well. For example, the seed points have to be selected manually and this manual selection of seed points at the time of segmentation brings about wrong selection of regions. So, a review of some automatic seed selection methods with their advantages, disadvantages and applications in different fields has been presented.

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

Remote Sensing ◽

10.3390/rs11232727 ◽

2019 ◽

Vol 11 (23) ◽

pp. 2727 ◽

Cited By ~ 2

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.

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SEGMENTATION OF LARGE UNSTRUCTURED POINT CLOUDS USING OCTREE-BASED REGION GROWING AND CONDITIONAL RANDOM FIELDS

ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences ◽

10.5194/isprs-archives-xlii-2-w8-25-2017 ◽

2017 ◽

Vol XLII-2/W8 ◽

pp. 25-30 ◽

Cited By ~ 12

Author(s):

M. Bassier ◽

M. Bonduel ◽

B. Van Genechten ◽

M. Vergauwen

Keyword(s):

Random Fields ◽

Point Cloud ◽

Conditional Random Fields ◽

Conditional Random Field ◽

Region Growing ◽

Point Clouds ◽

Single Type ◽

Point Cloud Data ◽

Cloud Data ◽

Point Cloud Segmentation

Point cloud segmentation is a crucial step in scene understanding and interpretation. The goal is to decompose the initial data into sets of workable clusters with similar properties. Additionally, it is a key aspect in the automated procedure from point cloud data to BIM. Current approaches typically only segment a single type of primitive such as planes or cylinders. Also, current algorithms suffer from oversegmenting the data and are often sensor or scene dependent.<br><br> In this work, a method is presented to automatically segment large unstructured point clouds of buildings. More specifically, the segmentation is formulated as a graph optimisation problem. First, the data is oversegmented with a greedy octree-based region growing method. The growing is conditioned on the segmentation of planes as well as smooth surfaces. Next, the candidate clusters are represented by a Conditional Random Field after which the most likely configuration of candidate clusters is computed given a set of local and contextual features. The experiments prove that the used method is a fast and reliable framework for unstructured point cloud segmentation. Processing speeds up to 40,000 points per second are recorded for the region growing. Additionally, the recall and precision of the graph clustering is approximately 80%. Overall, nearly 22% of oversegmentation is reduced by clustering the data. These clusters will be classified and used as a basis for the reconstruction of BIM models.

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Three-Dimensional Point Cloud Segmentation Algorithm Based on Improved Region Growing

Laser & Optoelectronics Progress ◽

10.3788/lop55.051502 ◽

2018 ◽

Vol 55 (5) ◽

pp. 051502

Author(s):

李仁忠 Li Renzhong ◽

刘阳阳 Liu Yangyang ◽

杨曼 Yang Man ◽

张缓缓 Zhang Huanhuan

Keyword(s):

Point Cloud ◽

Three Dimensional ◽

Region Growing ◽

Segmentation Algorithm ◽

Point Cloud Segmentation

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Octree-based region growing for point cloud segmentation

ISPRS Journal of Photogrammetry and Remote Sensing ◽

10.1016/j.isprsjprs.2015.01.011 ◽

2015 ◽

Vol 104 ◽

pp. 88-100 ◽

Cited By ~ 189

Author(s):

Anh-Vu Vo ◽

Linh Truong-Hong ◽

Debra F. Laefer ◽

Michela Bertolotto

Keyword(s):

Point Cloud ◽

Region Growing ◽

Point Cloud Segmentation

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Region Growing Based on 2D -3D Mutual Projections for Visible Point Cloud Segmentation

IEEE Transactions on Instrumentation and Measurement ◽

10.1109/tim.2021.3080385 ◽

2021 ◽

pp. 1-1

Author(s):

Wanning Zhang ◽

Fuqiang Zhou ◽

Lin Wang ◽

Pengfei Sun

Keyword(s):

Point Cloud ◽

Region Growing ◽

Point Cloud Segmentation

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Roof Plane Segmentation from Airborne LiDAR Data Using Hierarchical Clustering and Boundary Relabeling

Remote Sensing ◽

10.3390/rs12091363 ◽

2020 ◽

Vol 12 (9) ◽

pp. 1363 ◽

Cited By ~ 1

Author(s):

Li Li ◽

Jian Yao ◽

Jingmin Tu ◽

Xinyi Liu ◽

Yinxuan Li ◽

...

Keyword(s):

Hierarchical Clustering ◽

Three Dimensional ◽

Region Growing ◽

Point Clouds ◽

Airborne Lidar ◽

Lidar Data ◽

Building Models ◽

Novel Approach ◽

Airborne Lidar Data ◽

Seed Points

The roof plane segmentation is one of the key issues for constructing accurate three-dimensional building models from airborne light detection and ranging (LiDAR) data. Region growing is one of the most widely used methods to detect roof planes. It first selects one point or region as a seed, and then iteratively expands to neighboring points. However, region growing has two problems. The first problem is that it is hard to select the robust seed points. The other problem is that it is difficult to detect the accurate boundaries between two roof planes. In this paper, to solve these two problems, we propose a novel approach to segment the roof planes from airborne LiDAR point clouds using hierarchical clustering and boundary relabeling. For the first problem, we first extract the initial set of robust planar patches via an octree-based method, and then apply the hierarchical clustering method to iteratively merge the adjacent planar patches belonging to the same plane until the merging cost exceeds a predefined threshold. These merged planar patches are regarded as the robust seed patches for the next region growing. The coarse roof planes are generated by adding the non-planar points into the seed patches in sequence using region growing. However, the boundaries of coarse roof planes may be inaccurate. To solve this problem, namely, the second problem, we refine the boundaries between adjacent coarse planes by relabeling the boundary points. At last, we can effectively extract high-quality roof planes with smooth and accurate boundaries from airborne LiDAR data. We conducted our experiments on two datasets captured from Vaihingen and Wuhan using Leica ALS50 and Trimble Harrier 68i, respectively. The experimental results show that our proposed approach outperforms several representative approaches in both visual quality and quantitative metrics.

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