Fast and Accurate Plane Segmentation of Airborne LiDAR Point Cloud Using Cross-Line Elements

Wu, Teng; Hu, Xiangyun; Ye, Lizhi

doi:10.3390/rs8050383

Cited by 15 publications

(9 citation statements)

References 40 publications

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“…Several approaches are proposed in the literature for roof extraction based on LiDAR point clouds. The extraction of roof planes can be performed for example by using cross-line elements (Wu et al, 2016), the RANSAC algorithm (Tarsha-Kurdi et al, 2008), a combination between a Principal Component Analysis and a robust normal estimation method (Gilani et al, 2016), or even by analysing layers of the point cloud to detect roof ridges (Fan et al, 2014).…”

Section: Extraction Of Chimneys and Roof Ridgesmentioning

confidence: 99%

Photogrammetric Recording and Reconstruction of Town Scale Models – The Case of the Plan-Relief of Strasbourg

Macher

Grussenmeyer

Landes

et al. 2017

Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci.

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ABSTRACT:The French collection of Plan-Reliefs, scale models of fortified towns, constitutes a precious testimony of the history of France. The aim of the URBANIA project is the valorisation and the diffusion of this Heritage through the creation of virtual models. The town scale model of Strasbourg at 1/600 currently exhibited in the Historical Museum of Strasbourg was selected as a case study. In this paper, the photogrammetric recording of this scale model is first presented. The acquisition protocol as well as the data postprocessing are detailed. Then, the modelling of the city and more specially building blocks is investigated. Based on point clouds of the scale model, the extraction of roof elements is considered. It deals first with the segmentation of the point cloud into building blocks. Then, for each block, points belonging to roofs are identified and the extraction of chimney point clouds as well as roof ridges and roof planes is performed. Finally, the 3D parametric modelling of the building blocks is studied by considering roof polygons and polylines describing chimneys as input. In a future works section, the semantically enrichment and the potential usage scenarios of the scale model are envisaged.

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Section: Extraction Of Chimneys and Roof Ridgesmentioning

confidence: 99%

Photogrammetric Recording and Reconstruction of Town Scale Models – The Case of the Plan-Relief of Strasbourg

Macher

Grussenmeyer

Landes

et al. 2017

Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci.

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“…Noteworthy that these transform estimators has been successfully used in many remote sensing tasks, such as fundamental matrix estimation with only translation and radial distortion [45], multi-modal correspondence [46], Quasi-Homography transform estimation for wide baseline stereo [47], and rotation-scaling-translation estimation based on fractal image model [48]. In particular, robust estimators are suitable for the model fitting task in point clouds, such as plane and roof reconstruction [49][50][51], road fitting and segmentation [52,53], etc. Apart from the "fit-and-remove" scheme, there are several strategies for estimating transformation.…”

Section: Related Workmentioning

confidence: 99%

A Robust Transform Estimator Based on Residual Analysis and Its Application on UAV Aerial Images

Cai

Leng

et al. 2018

Remote Sensing

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Abstract:Estimating the transformation between two images from the same scene is a fundamental step for image registration, image stitching and 3D reconstruction. State-of-the-art methods are mainly based on sorted residual for generating hypotheses. This scheme has acquired encouraging results in many remote sensing applications. Unfortunately, mainstream residual based methods may fail in estimating the transform between Unmanned Aerial Vehicle (UAV) low altitude remote sensing images, due to the fact that UAV images always have repetitive patterns and severe viewpoint changes, which produce lower inlier rate and higher pseudo outlier rate than other tasks. We performed extensive experiments and found the main reason is that these methods compute feature pair similarity within a fixed window, making them sensitive to the size of residual window. To solve this problem, three schemes that based on the distribution of residuals are proposed, which are called Relational Window (RW), Sliding Window (SW), Reverse Residual Order (RRO), respectively. Specially, RW employs a relaxation residual window size to evaluate the highest similarity within a relaxation model length. SW fixes the number of overlap models while varying the length of window size. RRO takes the permutation of residual values into consideration to measure similarity, not only including the number of overlap structures, but also giving penalty to reverse number within the overlap structures. Experimental results conducted on our own built UAV high resolution remote sensing images show that the proposed three strategies all outperform traditional methods in the presence of severe perspective distortion due to viewpoint change.

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“…In buildings, reconstruction usually contains two key processes: building roof edge segmentation and topological reconstruction [1]. Since plane features of buildings are more stable than point or line features, for complex roof structures with high-density point cloud data, methods based on plane segmentation is first adopted, such as ridge or edge-based and voxel-based region growing [10,11], cross-line element growth (CLEG) [12], RANSAC [13,14], classification or feature clustering [15][16][17][18]; then, point or line features are obtained method [5] belongs to data-driven while Guo's approach [2] belongs to model-driven. However, differed from other man-made objects, the power pylon in high voltage systems is well-designed with specific structures, which is much more complex in structure and diverse in type, leading the existing object reconstruction approaches could not be directly applicable for the pylon problem.…”

Section: Related Workmentioning

confidence: 99%

“…2017, 9, 1172 17 of 24 To quantitatively evaluate the accuracy of head reconstruction results, the number of key points extracted by an alpha shape algorithm ncount, the average distance Dave and maximum distance Dmax between the reconstructed head model and the original point cloud are, respectively, calculated according to Equations (12) and (13). To quantitatively evaluate the accuracy of head reconstruction results, the number of key points extracted by an alpha shape algorithm ncount, the average distance D ave and maximum distance D max between the reconstructed head model and the original point cloud are, respectively, calculated according to Equations (12) and (13).…”

Section: Precision Of Pylon Head Reconstructionmentioning

confidence: 99%

A Heuristic Method for Power Pylon Reconstruction from Airborne LiDAR Data

et al. 2017

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Abstract:Object reconstruction from airborne LiDAR data is a hot topic in photogrammetry and remote sensing. Power fundamental infrastructure monitoring plays a vital role in power transmission safety. This paper proposes a heuristic reconstruction method for power pylons widely used in high voltage transmission systems from airborne LiDAR point cloud, which combines both data-driven and model-driven strategies. Structurally, a power pylon can be decomposed into two parts: the pylon body and head. The reconstruction procedure assembles two parts sequentially: firstly, the pylon body is reconstructed by a data-driven strategy, where a RANSAC-based algorithm is adopted to fit four principal legs; secondly, a model-driven strategy is used to reconstruct the pylon head with the aid of a predefined 3D head model library, where the pylon head's type is recognized by a shape context algorithm, and their parameters are estimated by a Metropolis-Hastings sampler coupled with a Simulated annealing algorithm. The proposed method has two advantages: (1) optimal strategies are adopted to reconstruct different pylon parts, which are robust to noise and partially missing data; and (2) both the number of parameters and their search space are greatly reduced when estimating the head model's parameters, as the body reconstruction results information about the original point cloud, and relationships between parameters are used in the pylon head reconstruction process. Experimental results show that the proposed method can efficiently reconstruct power pylons, and the average residual between the reconstructed models and the raw data was smaller than 0.3 m.

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Fast and Accurate Plane Segmentation of Airborne LiDAR Point Cloud Using Cross-Line Elements

Cited by 15 publications

References 40 publications

Photogrammetric Recording and Reconstruction of Town Scale Models – The Case of the Plan-Relief of Strasbourg

Photogrammetric Recording and Reconstruction of Town Scale Models – The Case of the Plan-Relief of Strasbourg

A Robust Transform Estimator Based on Residual Analysis and Its Application on UAV Aerial Images

A Heuristic Method for Power Pylon Reconstruction from Airborne LiDAR Data

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