Robust 3-D Plane Segmentation From Airborne Point Clouds Based on <i>Quasi-A-Contrario</i> Theory

Zhu, Xianzhang; Liu, Xinyi; Zhang, Yongjun; Duan, Yun‐Bo

doi:10.1109/jstars.2021.3093576

Cited by 11 publications

(5 citation statements)

References 58 publications

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“…The proposed approach was implemented with C++ in Windows and carried out on a computer with an Intel Core i9-9900K processor and 64 GB of RAM. In addition, qualitative comparisons are performed between the proposed method and four popular and recent approaches: region-growing (RG), RANSCA, PLINKAGE [53], and QTPS [58]. The region-growing and RANSAC methods are the most popular, widely adopted algorithms for point cloud segmentation.…”

Section: Experiments and Discussionmentioning

confidence: 99%

An Automatic Hierarchical Clustering Method for the LiDAR Point Cloud Segmentation of Buildings via Shape Classification and Outliers Reassignment

et al. 2023

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Shape segmentation in urban environments forms the foundation for tasks such as classification and reconstruction. Most artificial buildings with complex structures are composed of multiple simple geometric primitives. Based on this assumption, this paper proposes a divisive hierarchical clustering algorithm that uses shape classification and outliers reassignment to segment LiDAR point clouds in order to effectively identify the various shapes of structures that make up buildings. The proposed method adopts a coarse-to-fine strategy. Firstly, based on the geometric properties of different primitives in a Gaussian sphere space, coarse extraction is performed using Gaussian mapping and the DBSCAN algorithm to identify the primary structure of various shapes. Then, the error functions are constructed after parameterizing the recognized shapes. Finally, a minimum energy loss function is built by combining the error functions and binary integer programming (BIP) to redistribute the outlier points. Thereby, the accurate extraction of geometric primitives is achieved. Experimental evaluations on real point cloud datasets show that the indicators of precision, accuracy, and F1 score of our method are 0.98, 0.95, and 0.96 (point assignment) and 0.97, 0.95, and 0.95 (shape recognition), respectively. Compared with other state-of-the-art methods, the proposed method can efficiently segment planar and non-planar structures with higher quality from building point clouds.

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Section: Experiments and Discussionmentioning

confidence: 99%

An Automatic Hierarchical Clustering Method for the LiDAR Point Cloud Segmentation of Buildings via Shape Classification and Outliers Reassignment

et al. 2023

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“…In contrast to that, plane segmentation for 3D points is less constrained by the adjacency relationship so is able to detect complete planar structures better. A novel plane segmentation method (QTPS), which was introduced in our previous paper and proved to be efficient and robust (Zhu et al, 2021), is employed in this part to partition mesh vertices into planes. For each segmented vertex subset, we collect the 1‐ring neighbour faces of each inlier vertex to construct the plane‐support region.…”

Section: Methodsmentioning

confidence: 99%

Generation of concise 3D building model from dense meshes by extracting and completing planar primitives

Liu

Zhu

Zhang

et al. 2023

The Photogrammetric Record

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The generation of a concise building model has been and continues to be a challenge in photogrammetry and computer graphics. The current methods typically focus on the simplicity and fidelity of the model, but those methods either fail to preserve the structural information or suffer from low computational efficiency. In this paper, we propose a novel method to generate concise building models from dense meshes by extracting and completing the planar primitives of the building. From the perspective of probability, we first extract planar primitives from the input mesh and obtain the adjacency relationships between the primitives. Since primitive loss and structural defects are inevitable in practice, we employ a novel structural completion approach to eliminate linkage errors. Finally, the concise polygonal mesh is reconstructed by connectivitybased primitive assembling. Our method is efficient and robust to various challenging data. Experiments on various building models revealed the efficacy and applicability of our method.

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“…Xu et al proposed an optimal-vector field based method for LiDAR point cloud data [33], in which a local phase formulated a new optimal-vector field to detect plane intersections, and a global phase smoothed local segmentation cues to mimic leading eigenvector computation in the graph-cut. Zhu et al proposed a quasi-acontrario theory-based plane segmentation algorithm, which was capable of dealing with point clouds of severe noise level, low density, and high complexity robustly [34].…”

Section: E Hybrid Methodsmentioning

confidence: 99%

Roof Plane Segmentation From LiDAR Point Cloud Data Using Region Expansion Based L ₀ Gradient Minimization and Graph Cut

Wang¹,

Ji²

2021

IEEE J. Sel. Top. Appl. Earth Observations Remote Sensing

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Automatic roof segmentation from airborne light detection and ranging (LiDAR) point cloud data is a key technology for building reconstruction and digital city modeling. In this paper, we develop a novel region expansion based L0 gradient minimization algorithm for processing unordered point cloud data, and a two-stage global optimization method consisting of the L0 gradient minimization and graph cut for roof plane segmentation. Firstly, we extract the LiDAR points of buildings from the original point cloud data with a deep learning based method and separate the points of the different buildings using Euclidean clustering to improve the processing efficiency. Secondly, region expansion based L0 gradient minimization is proposed, which is specially designed for roof plane segmentation from unordered point clouds. To fundamentally avoid the need for empirical parameter tuning in L0 gradient minimization, we propose a multi-stage coarseto-fine segmentation process, which further improves the effect of the roof plane segmentation. Finally, graph cut is utilized to solve the jagged boundary and over-segmentation problems existing in the segmented roof planes and produce the segmentation results. We conducted comparative experiments on the Vaihingen and Hangzhou datasets. The experimental results show that the proposed approach significantly outperforms the current state-of-the-art approaches at least 6.7% and 8.9% in roof plane quality index in the Vaihingen and Hangzhou datasets, while showing superior robustness to different kinds of data.

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Robust 3-D Plane Segmentation From Airborne Point Clouds Based on Quasi-A-Contrario Theory

Cited by 11 publications

References 58 publications

An Automatic Hierarchical Clustering Method for the LiDAR Point Cloud Segmentation of Buildings via Shape Classification and Outliers Reassignment

An Automatic Hierarchical Clustering Method for the LiDAR Point Cloud Segmentation of Buildings via Shape Classification and Outliers Reassignment

Generation of concise 3D building model from dense meshes by extracting and completing planar primitives

Roof Plane Segmentation From LiDAR Point Cloud Data Using Region Expansion Based L ₀ Gradient Minimization and Graph Cut

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Robust 3-D Plane Segmentation From Airborne Point Clouds Based on Quasi-A-Contrario Theory

Cited by 11 publications

References 58 publications

An Automatic Hierarchical Clustering Method for the LiDAR Point Cloud Segmentation of Buildings via Shape Classification and Outliers Reassignment

An Automatic Hierarchical Clustering Method for the LiDAR Point Cloud Segmentation of Buildings via Shape Classification and Outliers Reassignment

Generation of concise 3D building model from dense meshes by extracting and completing planar primitives

Roof Plane Segmentation From LiDAR Point Cloud Data Using Region Expansion Based L 0 Gradient Minimization and Graph Cut

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Roof Plane Segmentation From LiDAR Point Cloud Data Using Region Expansion Based L ₀ Gradient Minimization and Graph Cut