An Automatic Hierarchical Clustering Method for the LiDAR Point Cloud Segmentation of Buildings via Shape Classification and Outliers Reassignment
Abstract
:1. Introduction
- (1)
- A novel framework for building segmentation via shape classification and outliers reassignment is presented.
- (2)
- A coarse segmentation method based on a combination of Gaussian mapping and DBSCAN is developed. The plane, cone, cylinder, and sphere structures that make up the target are identified based on the morphological differences of different shape primitives presented in Gaussian spherical space.
- (3)
- A fine segmentation based on the BIP models is proposed. The complete segmentation is achieved by the constructed minimum energy function, which reassigns the outlier points to each recognized primitive.
2. Related Works
2.1. Region Growing
2.2. Model Fitting
2.3. Clustering
2.4. Hierarchical Clustering
3. Methods
3.1. Overview of the Proposed Approach
3.2. Coarse Segmentation
- (1)
- The DBSCAN algorithm is executed in the Gaussian sphere space to obtain multiple clusters ς = {G1, G2, …, Gi};
- (2)
- For a cluster Gi, a spatial resampling is performed with an interval of 0.01 m, and then a point in Gi is selected and searched for points within the radius of r/2;
- (3)
- After calculating the eigenvalues of the searched points, if all three eigenvalues are less than 1, Gi is a spot, and the corresponding point groups in the 3D point cloud space are marked as planes. Then, the shape features are derived using Equation (1); if Pλ < Lλ, Lλ is linear, its radius is calculated. If the radius is 1 m, the corresponding points groups in the 3D point cloud space are marked as cylinders. If the radius is less than 1 m, those groups are marked as cones. Note that when Gi is judged to be linear, this cluster may contain another spot cluster, as shown in Figure 3. The generated local highlight phenomenon is evident in the circle of Figure 3c, so we count the average value of NN for the core points in Gi. If the NN of the core point is larger than the average value, it and its neighbors are marked as local highlighting regions and selected as a new cluster;
- (4)
- The 3D spherical surface is also presented as a uniformly distributed spherical structure in the Gaussian sphere space. Thus, for the points of the spherical surface, the NN of core points should be close. In the Gaussian sphere space, after excluding the identified points, the remaining points connected and have close NN values to each other are selected as clusters and marked as spherical shapes.
3.3. Fine Segmentation
3.3.1. Shape Parameterization
Planes
Cylinder
Cone and Sphere
3.3.2. Outlier Reassignment
Fitting
Connectivity
Consistency
4. Experiments and Discussion
4.1. Evaluation Metrics
4.2. Experiments on Synthetic Data
4.3. Experiments on Real Point Cloud Datasets
4.3.1. Qualitative Evaluations
4.3.2. Quantitative Evaluations
5. Conclusions
Author Contributions
Funding
Data Availability Statement
Acknowledgments
Conflicts of Interest
References
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Synthetic Dataset | Number of Points | Number of Shapes | Average Point Distance (m) |
---|---|---|---|
Cube | 12,000 | 6 | 0.04 |
Cylinder | 7999 | 3 | 0.05 |
Cone | 7993 | 2 | 0.03 |
Pyramid | 10,000 | 5 | 0.03 |
Hollow cube | 20,019 | 7 | 0.03 |
Torus | 20,006 | 24 | 0.03 |
Datasets | Areas | Sensors | Flying Height (m) | Acquired Date (Month, Year) | Number of Points | Number of Shapes | Average Point Distance (m) |
---|---|---|---|---|---|---|---|
#1 | Vaihingen | Leica ALS50 | 500 | 08, 2008 | 10,488 | 6 | 0.48 |
#2 | Vaihingen | Leica ALS50 | 500 | 08, 2008 | 926 | 6 | 0.65 |
#3 | Vaihingen | Leica ALS50 | 500 | 08, 2008 | 952 | 9 | 0.65 |
#4 | Vaihingen | Leica ALS50 | 500 | 08, 2008 | 1412 | 8 | 0.7 |
#5 | Vaihingen | Leica ALS50 | 500 | 08, 2008 | 3827 | 8 | 0.65 |
#6 | Toronto | ALTM-Orion M | 650 | 02, 2009 | 44,094 | 36 | 0.6 |
#7 | Toronto | ALTM-Orion M | 650 | 02, 2009 | 38,601 | 28 | 0.75 |
#8 | Toronto | ALTM-Orion M | 650 | 02, 2009 | 12,574 | 9 | 0.8 |
#9 | Toronto | ALTM-Orion M | 650 | 02, 2009 | 56,267 | 24 | 0.75 |
#10 | Toronto | ALTM-Orion M | 650 | 02, 2009 | 59,053 | 10 | 0.7 |
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Wang, F.; Zhou, G.; Xie, J.; Fu, B.; You, H.; Chen, J.; Shi, X.; Zhou, B. An Automatic Hierarchical Clustering Method for the LiDAR Point Cloud Segmentation of Buildings via Shape Classification and Outliers Reassignment. Remote Sens. 2023, 15, 2432. https://doi.org/10.3390/rs15092432
Wang F, Zhou G, Xie J, Fu B, You H, Chen J, Shi X, Zhou B. An Automatic Hierarchical Clustering Method for the LiDAR Point Cloud Segmentation of Buildings via Shape Classification and Outliers Reassignment. Remote Sensing. 2023; 15(9):2432. https://doi.org/10.3390/rs15092432
Chicago/Turabian StyleWang, Feng, Guoqing Zhou, Jiali Xie, Bolin Fu, Haotian You, Jianjun Chen, Xue Shi, and Bowen Zhou. 2023. "An Automatic Hierarchical Clustering Method for the LiDAR Point Cloud Segmentation of Buildings via Shape Classification and Outliers Reassignment" Remote Sensing 15, no. 9: 2432. https://doi.org/10.3390/rs15092432