Efficient maximal Poisson-disk sampling and remeshing on surfaces

Guo, Jianwei; Yan, Dong‐Ming; Jia, Xiaohong; Zhang, Xiaopeng

doi:10.1016/j.cag.2014.09.015

Cited by 44 publications

(34 citation statements)

References 39 publications

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“…Poisson-disk sampling satisfies three properties: the distance between any two disk centers should be larger than the sampling radius; the union of the disks should cover the entire sampling domain; each point in the domain has a probability that is proportional to the sizing at this point to receive a sampling point [Guo et al 2015]. We used the Poisson-disk sampling algorithm to reduce the number of points in the point cloud.…”

Section: Samplingmentioning

confidence: 99%

Automatic planar shape segmentation from indoor point clouds

Shui

Liu

Ren

et al. 2016

Proceedings of the 15th ACM SIGGRAPH Conference on Virtual-Reality Continuum and Its Applications in Industry - Volume 1

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Figure 1: Planar shapes segmentation and virtual roaming. From the sampled scanned point clouds, the planar shapes of indoor scene (such as floor, walls, ceilings, etc.) have been finely segmentation. Subsequently, the indoor scene is represented by only fewer number of planar patches and the digital model with texture is imported to Unity 3D to enable members of the public virtually experience the space. AbstractThe use of a terrestrial laser scanner (TLS) has become a popular technique for the acquisition of 3D scenes in architecture and design. Surface reconstruction is used to generate a digital model from the acquired point clouds. However, the model often consists of excessive data, limiting real-time user experiences that make use of the model. In this study, we present a coarse to fine planar shape segmentation method for indoor point clouds, which results in the digital model of an indoor scene being represented by a small number of planar patches. First, the Gaussian map and region growing techniques are used to coarsely segment the planar shape from sampled point clouds. Then, the best-fit-plane is calculated by random sample consensus (RANSAC), avoiding the negative impact of outliers. Finally, the refinement of planar shape is produced by projecting point clouds onto the corresponding bestfit-plane. Our method has been demonstrated to be robust towards noise and outliers in the scanned point clouds and overcomes the limitations of over-and under-segmentation. We have tested our system and algorithms on real datasets and experiments show the reliability of the proposed method against existing region-growing methods.

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Section: Samplingmentioning

confidence: 99%

Automatic planar shape segmentation from indoor point clouds

Shui

Liu

Ren

et al. 2016

Proceedings of the 15th ACM SIGGRAPH Conference on Virtual-Reality Continuum and Its Applications in Industry - Volume 1

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show abstract

“…However, none of them works reliably for all kinds of meshes. Most remeshing algorithms avoid this problem by assuming that the features have been specified in advance [8], [9], [19], [21], [22], [33]. Some remeshing techniques try to preserve features implicitly [34], [35].…”

Section: Related Workmentioning

confidence: 99%

“…Yan et al [8], [19], [20] avoid the parameterization by computing the 3D CVT restricted to the surface. Additionally, they proposed blue-noise remeshing techniques using adaptive maximal Poisson-disk sampling [9], [21], farthest point optimization [22], and push-pull operations [23], which improve the element quality as well as introducing blue-noise properties. However, these approaches still suffer from common limitations, e.g., geometric fidelity and the minimal angle cannot be explicitly bounded.…”

Section: Related Workmentioning

confidence: 99%

Error-Bounded and Feature Preserving Surface Remeshing with Minimal Angle Improvement

Yan

Bommes

et al. 2017

IEEE Trans. Visual. Comput. Graphics

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Abstract-The typical goal of surface remeshing consists in finding a mesh that is (1) geometrically faithful to the original geometry, (2) as coarse as possible to obtain a low-complexity representation and (3) free of bad elements that would hamper the desired application. In this paper, we design an algorithm to address all three optimization goals simultaneously. The user specifies desired bounds on approximation error δ, minimal interior angle θ and maximum mesh complexity N (number of vertices). Since such a desired mesh might not even exist, our optimization framework treats only the approximation error bound δ as a hard constraint and the other two criteria as optimization goals. More specifically, we iteratively perform carefully prioritized local operators, whenever they do not violate the approximation error bound and improve the mesh otherwise. Our optimization framework greedily searches for the coarsest mesh with minimal interior angle above θ and approximation error bounded by δ. Fast runtime is enabled by a local approximation error estimation, while implicit feature preservation is obtained by specifically designed vertex relocation operators. Experiments show that our approach delivers high-quality meshes with implicitly preserved features and better balances between geometric fidelity, mesh complexity and element quality than the state-of-the-art.Index Terms-surface remeshing, error-bounded, feature preserving, minimal angle improvement, feature intensity.

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“…Yan and Wonka [16] recently presented maximal Poissondisk sampling (MPS) on surfaces based on empty region analysis. Guo et al [17] then improved the sampling quality and efficiency of MPS by using a hierarchical subdivision based approach. Iterative relaxation is another important technique for generating high-quality point distributions.…”

Section: Samplingmentioning

confidence: 99%

“…We applied our approach to analyze several sampling algorithms on surfaces, including CVT [21], CapCVT [20], MPS [16,17], and FPO [25]. Poisson sampling is used for ground truth, where g(r) = 1.…”

Section: Sampling Analysismentioning

confidence: 99%

Analyzing surface sampling patterns using the localized pair correlation function

et al. 2016

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Point distributions with different characteristics have a crucial influence on graphics applications. Various analysis tools have been developed in recent years, mainly for blue noise sampling in Euclidean domains. In this paper, we present a new method to analyze the properties of general sampling patterns that are distributed on mesh surfaces. The core idea is to generalize to surfaces the pair correlation function (PCF) which has successfully been employed in sampling pattern analysis and synthesis in 2D and 3D. Experimental results demonstrate that the proposed approach can reveal correlations of point sets generated by a wide range of sampling algorithms. An acceleration technique is also suggested to improve the performance of the PCF.

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Efficient maximal Poisson-disk sampling and remeshing on surfaces

Cited by 44 publications

References 39 publications

Automatic planar shape segmentation from indoor point clouds

Automatic planar shape segmentation from indoor point clouds

Error-Bounded and Feature Preserving Surface Remeshing with Minimal Angle Improvement

Analyzing surface sampling patterns using the localized pair correlation function

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