Fast regularity-constrained plane fitting

Lin, Yangbin; Li, Jialian; Wang, Cheng; Chen, Zhonggui; Wang, Zongyue; Li, Jonathan

doi:10.1016/j.isprsjprs.2020.01.009

Cited by 15 publications

(12 citation statements)

References 43 publications

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“…Lin et al applied L0 gradient minimization [29] on regularity-constrained plane fitting, which does not need initial segmentation results [30]. However, the L0 gradient minimization does not consider the geometric spatial information of unordered data.…”

Section: Global Energy Optimization Based Methodsmentioning

confidence: 99%

“…To verify the efficiency of their proposed L0 gradient minimization algorithm, they applied it in both 2D image denoising and 3D mesh denoising. [30] subsequently applied the algorithm to 3D plane fitting, where the input signal is the normal vectors of the point cloud. In this fitting problem, the angle difference between two normal vectors reflects the discrepancy between signals and the neighboring points with similar normal vectors are merged.…”

Section: S S S S I I S Smentioning

confidence: 99%

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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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Section: Global Energy Optimization Based Methodsmentioning

confidence: 99%

Section: S S S S I I S Smentioning

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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“…Moreover, these methods (PEARL, T-linkage and CORAL (Isack & Boykov, 2012;Magri & Fusiello, 2014;Amayo et al, 2018)) have shown their efficiency in solving 2D multi-model fitting problems and homographies, but do not show extensive experimentation in the reconstruction of 3D multiple plane-based models with geometric constraints. Lin et al (2020) formulated the problem as a global gradient minimization, proposing an updated method (Global-L0) based on a constraint model that outperforms traditional plane fitting methods.…”

Section: Related Workmentioning

confidence: 99%

“…We propose a method for plane-based models reconstructed from a set of 3D points, taking advantage of the geometric constraints that are present in the original scene, exhibiting high accuracy in the presence of noise and outliers, and reducing the processing time. We selected the most representative methods for comparison: LSE (Mitra & Nguyen, 2003) and RANSAC (Fischler & Bolles, 1981) as classic baseline methods and MC-RANSAC (Saval-Calvo et al, 2015a), RSPD (Araújo & Oliveira, 2020), Prior-MLESAC (Zhao et al, 2020), Prog-X (Barath & Matas, 2019;Barath et al, 2020) and Global-L0 (Lin et al, 2020) are the most recent, providing a wide range of methods.…”

Section: Related Workmentioning

confidence: 99%

“…Full-size  DOI: 10.7717/peerj-cs.691/fig-7 (Fischler & Bolles, 1981) as baseline methods. We also compared MC-LSE with state-ofthe-art methods such as multi-constraint RANSAC (MC-RANSAC) (Saval-Calvo et al, 2015a), RSPD (Araújo & Oliveira, 2020), Prior-MLESAC (Zhao et al, 2020), Prog-X (Barath et al, 2020), and Global-L0 (Lin et al, 2020), providing an extensive comparison of our method.…”

Section: Experimental Comparisonmentioning

confidence: 99%

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Iterative multilinear optimization for planar model fitting under geometric constraints

Azorín-López

Sebban

Fuster-Guilló

et al. 2021

PeerJ Computer Science

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Planes are the core geometric models present everywhere in the three-dimensional real world. There are many examples of manual constructions based on planar patches: facades, corridors, packages, boxes, etc. In these constructions, planar patches must satisfy orthogonal constraints by design (e.g. walls with a ceiling and floor). The hypothesis is that by exploiting orthogonality constraints when possible in the scene, we can perform a reconstruction from a set of points captured by 3D cameras with high accuracy and a low response time. We introduce a method that can iteratively fit a planar model in the presence of noise according to three main steps: a clustering-based unsupervised step that builds pre-clusters from the set of (noisy) points; a linear regression-based supervised step that optimizes a set of planes from the clusters; a reassignment step that challenges the members of the current clusters in a way that minimizes the residuals of the linear predictors. The main contribution is that the method can simultaneously fit different planes in a point cloud providing a good accuracy/speed trade-oﬀ even in the presence of noise and outliers, with a smaller processing time compared with previous methods. An extensive experimental study on synthetic data is conducted to compare our method with the most current and representative methods. The quantitative results provide indisputable evidence that our method can generate very accurate models faster than baseline methods. Moreover, two case studies for reconstructing planar-based objects using a Kinect sensor are presented to provide qualitative evidence of the efficiency of our method in real applications.

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Regularity-constrained point cloud reconstruction of building models via global alignment

Yu,

Cao,

Liu

et al. 2024

Vis Comput

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Fast regularity-constrained plane fitting

Cited by 15 publications

References 43 publications

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

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

Iterative multilinear optimization for planar model fitting under geometric constraints

Regularity-constrained point cloud reconstruction of building models via global alignment

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Fast regularity-constrained plane fitting

Cited by 15 publications

References 43 publications

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

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

Iterative multilinear optimization for planar model fitting under geometric constraints

Regularity-constrained point cloud reconstruction of building models via global alignment

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

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