A new approach for roof segmentation from airborne LiDAR point clouds

Zhao, Chuan; Guo, Hao; Lu, Jun; Yu, Donghang; Zhou, Xin; Lin, Yun

doi:10.1080/2150704x.2020.1847348

Cited by 5 publications

(4 citation statements)

References 11 publications

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“…During the process of growing, basic units near the rigid line are assigned to the first plane without considering the spatial coherence between units. In [47], [48], a weight function was constructed to solve the boundaries inaccuracy problem. In the weight function, both the number of points belonging to the involved roof planes among the neighborhoods of the boundary points and the orthogonal distance of the boundary points to the involved roof planes are considered.…”

Section: Boundaries Refinementmentioning

confidence: 99%

Roof Segmentation From Airborne LiDAR Using Octree-Based Hybrid Region Growing and Boundary Neighborhood Verification Voting

Liu

Zhang

et al. 2023

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

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Building roof segmentation is a key step in the process of three-dimensional (3D) building reconstruction using airborne Light Detection and Ranging (LiDAR) point cloud data. Voxel-based region growing is one of the most widely used methods to segment planes because of its high efficiency and easy implementation, but it is easy to omit roof planes due to the unreasonable voxel size and the complex roof structures. In addition, boundaries between adjacent roof planes are inaccurate. To solve the issues, a roof segmentation method using octree-based hybrid region growing and boundary neighborhood verification voting is proposed. First, an octree-based voxelization is performed on the raw building points to generate two basic units: planar voxels and individual points (i.e., points that are not in the planar voxels). Then, the hybrid region growing is conducted on these two basic units to segment coarse roof planes. A parameter-free boundary neighborhood verification voting strategy is used to assign the boundary points to the correct roof planes by verifying the neighborhoods of the boundary points and using reliable neighborhood information. Experimental results of four datasets, including two datasets provided by the International Society for Photogrammetry and Remote Sensing (ISPRS), and two high-density datasets provided by OpenTopography, verify that roof planes can be successfully segmented by the proposed method with over 96.8% completeness and a minimum 93.2% correctness. In addition, boundary points are assigned to the correct roof planes by the neighborhood verification voting strategy. Thus, the segmented roof planes can be used in various applications.

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Section: Boundaries Refinementmentioning

confidence: 99%

Roof Segmentation From Airborne LiDAR Using Octree-Based Hybrid Region Growing and Boundary Neighborhood Verification Voting

Liu

Zhang

et al. 2023

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

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“…Cloud Processing. TLS is mainly used as a data collector, and the data processor is point cloud processing [24][25][26]. e objective of point cloud processing is to convert 3D information into index for actual measurement, and specific processing technologies include point cloud filtering, point cloud segmentation, and point cloud fitting, among which point cloud segmentation is one research hotspot.…”

Section: Advances In Indoor Pointmentioning

confidence: 99%

Design and Research of Low‐Cost and Self‐Adaptive Terrestrial Laser Scanning for Indoor Measurement Based on Adaptive Indoor Measurement Scanning Strategy and Structural Characteristics Point Cloud Segmentation

Zhang

Zhou

Wang

et al. 2022

Advances in Civil Engineering

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Nowadays, TLS (terrestrial laser scanning) has been a relatively mature measuring equipment categorized to indoor measuring robots, but it is not widely adopted in indoor construction measurement at present. What accounts for its limited application are as follows: (1) the high cost of high-accuracy laser LIDAR and (2) existing TLS equipment does not possess self-adaptation scanning planning and takes no account of efficiency of point cloud processing and consumption of computing power. This paper proposes a novel TLS equipment and a high-efficiency point cloud processing method customized for the novel equipment, with purpose to achieve self-adaption measurement on the basis of indoor characteristics of construction during civil engineering at low cost. This paper mainly presents two parts of innovations: (1) for planning of scanning, the novel TLS features planning sampling density of scanning according to room size and converting scanning data from poses to point clouds, and (2) for processing of point clouds, this paper proposes two novel segmentation algorithms, namely, “on-boundary segmentation algorithm” and “on-angular-distance segmentation algorithm,” based on indoor spatial structure features and characteristics of TLS. Besides, this paper presents modified RANSAC-TLS (random sample consensus-total least squares) plane fitting algorithm, on basis of TLS point cloud distribution characteristics and spatial transformation. Through actual measurement test, it is concluded that the “on-boundary segmentation algorithm” and “on-angular-distance segmentation algorithm” are suitable for point cloud segmentation in different types of scenes. The modified RANSAC-TLS have made a great improvement on accuracy of fitting versus LS (least squares), TLS (total least squares), and RANSAC-LS. Finally, this paper conducts an experiment by executing an actual measurement and then preliminarily testifies the potential and future application of the proposed novel TLS (terrestrial laser scanning) equipment, with measurement parameters from it being changed in the experiment, by comparing with one existing TLS equipment—FARO. The test thus proves the relatively high feasibility and potential of the novel TLS presented in the paper (terrestrial laser scanning) in actual indoor measurement.

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“…Model-based segmentation algorithms divide the point cloud data with the same mathematical expression into homogeneous regions using the mathematical model of basic original geometric shapes as a priori knowledge. For example, existing modelbased point cloud segmentation approaches [12][13][14][15][16] are based on the development of the classical algorithm of random sampling consistency model fitting. The minimum variance estimation is used to calculate the model parameters of the random sample subset, and the deviation between the sample and the model is compared with the preset threshold, which can be used to detect the mathematical features such as line and circle.…”

Section: Related Workmentioning

confidence: 99%

3D Instance Segmentation and Object Detection Framework Based on the Fusion of Lidar Remote Sensing and Optical Image Sensing

Bai

Cen

et al. 2021

Remote Sensing

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Since single sensor and high-density point cloud data processing have certain direct processing limitations in urban traffic scenarios, this paper proposes a 3D instance segmentation and object detection framework for urban transportation scenes based on the fusion of Lidar remote sensing technology and optical image sensing technology. Firstly, multi-source and multi-mode data pre-fusion and alignment of Lidar and camera sensor data are effectively carried out, and then a unique and innovative network of stereo regional proposal selective search-driven DAGNN is constructed. Finally, using the multi-dimensional information interaction, three-dimensional point clouds with multi-features and unique concave-convex geometric characteristics are instance over-segmented and clustered by the hypervoxel storage in the remarkable octree and growing voxels. Finally, the positioning and semantic information of significant 3D object detection in this paper are visualized by multi-dimensional mapping of the boundary box. The experimental results validate the effectiveness of the proposed framework with excellent feedback for small objects, object stacking, and object occlusion. It can be a remediable or alternative plan to a single sensor and provide an essential theoretical and application basis for remote sensing, autonomous driving, environment modeling, autonomous navigation, and path planning under the V2X intelligent network space– ground integration in the future.

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A new approach for roof segmentation from airborne LiDAR point clouds

Cited by 5 publications

References 11 publications

Roof Segmentation From Airborne LiDAR Using Octree-Based Hybrid Region Growing and Boundary Neighborhood Verification Voting

Roof Segmentation From Airborne LiDAR Using Octree-Based Hybrid Region Growing and Boundary Neighborhood Verification Voting

Design and Research of Low‐Cost and Self‐Adaptive Terrestrial Laser Scanning for Indoor Measurement Based on Adaptive Indoor Measurement Scanning Strategy and Structural Characteristics Point Cloud Segmentation

3D Instance Segmentation and Object Detection Framework Based on the Fusion of Lidar Remote Sensing and Optical Image Sensing

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