How many sample points are sufficient for 3D model surface representation and accurate mesh simplification?

Asgharian, Lida; Ebrahimnezhad, Hossein

doi:10.1007/s11042-020-09395-3

Cited by 7 publications

(5 citation statements)

References 36 publications

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“…-Mesh-based methods: It is the preliminary adjustment of a PC to a surface, which undergoes successive simplification processes (Garland & Heckbert 1997). They are iterative methods, present inefficiency in profiling large portions and can be divided into two groups: based on decimation (Asgharian & Ebrahimnezhad 2020) or collapse (Hinderink, Mandad & Campen 2022). The first iteratively removes vertices and then performs a retriangulation process, while the second employs approaches based on edge blending to estimate an optimal position for a vertex (Asgharian & Ebrahimnezhad 2020).…”

Section: Related Researchmentioning

confidence: 99%

“…They are iterative methods, present inefficiency in profiling large portions and can be divided into two groups: based on decimation (Asgharian & Ebrahimnezhad 2020) or collapse (Hinderink, Mandad & Campen 2022). The first iteratively removes vertices and then performs a retriangulation process, while the second employs approaches based on edge blending to estimate an optimal position for a vertex (Asgharian & Ebrahimnezhad 2020). These methods assign a weightrelated to the importance of each point in the representation of a surface S based on statistical attributes, such as normal vectors (Hinderink, Mandad & Campen 2022) or the curvature of the point cloud (Asgharian & Ebrahimnezhad 2020).…”

Section: Related Researchmentioning

confidence: 99%

“…The first iteratively removes vertices and then performs a retriangulation process, while the second employs approaches based on edge blending to estimate an optimal position for a vertex (Asgharian & Ebrahimnezhad 2020). These methods assign a weightrelated to the importance of each point in the representation of a surface S based on statistical attributes, such as normal vectors (Hinderink, Mandad & Campen 2022) or the curvature of the point cloud (Asgharian & Ebrahimnezhad 2020). In addition, the determination of an energy function is commonly used to extract edge points (Hinderink, Mandad & Campen 2022).…”

Section: Related Researchmentioning

confidence: 99%

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3D Cartographic Generalization of LiDAR Point Clouds Based on the Principle of Self-Similarity of a Deterministic Fractal Structure

Freiman¹,

Santos

Reis

2023

Anu. Inst. Geociênc.

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The rendering of virtual three-dimensional (3D) structures represented by Point Cloud (PC) allows the representation of internal and/or external environments to buildings. However, the compilation of 3D geometric models is influenced by the intrinsic characteristics of PCs, which can be mitigated by the application of an PC simplification operator. According to the mathematical norms of fractal geometry, it was assumed that a PC is characterized by self-similarity. Two experimental datasets acquired with an SLT in static mode indoors were used. Four tasks were accomplished: sampling and structuring of a PC to solve the problem of random distribution, from an octree structure; estimation of the curvature of the points and the roughness of a neighbourhood for the extraction of edge points by the analysis of self-similarity and application of the Statistical Outliers Remove (SOR) algorithm, for the elimination of outliers points; uniform voxelization, to simplify the intermediate points; application of the Iterative Closest Point (ICP) algorithm to register the sets generated in the same local coordinate system. The use of voxelization was satisfactory, but once the voxel size is manually defined, the PC can be oversimplified and lose essential characteristics. This can be minimized by the primary analysis of the edge points, generating a set that is uniform, less noisy, and self- similar to the original set. To achieve a minimum density of points to model an environment three-dimensionally, one must analyse the geometric self- similarity characteristics of the PC to produce a simplified set self-similar to the original, considering the premises of fractal geometry. It is recommended to create an automatic simplification process to minimize the subjectivity coming from the analyst.

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Section: Related Researchmentioning

confidence: 99%

Section: Related Researchmentioning

confidence: 99%

Section: Related Researchmentioning

confidence: 99%

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3D Cartographic Generalization of LiDAR Point Clouds Based on the Principle of Self-Similarity of a Deterministic Fractal Structure

Freiman¹,

Santos

Reis

2023

Anu. Inst. Geociênc.

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“…Later, various new error functions were proposed based on the QEM algorithm to improve simplification quality. Asgharian et al reduced the number of vertices through resampling based on the Nyquist theorem [6], while preserving the important features of the model surface. Li et al introduced angle error control to simplify the model mesh based on the QEM algorithm for vertex curvature [7].…”

Section: Related Workmentioning

confidence: 99%

“…Li et al introduced angle error control to simplify the model mesh based on the QEM algorithm for vertex curvature [7]. However, literature [6], [7] considered simplification of the model texture information, and lacked consideration for the significant changes in LOD models during switching.…”

Section: Related Workmentioning

confidence: 99%

Research on Lightweighting and Rendering Optimization Techniques for Building Information Models

Tong,

Sun,

Tian

et al. 2023

J-NaNA

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Traditional Building Information Modeling (BIM) services, using a client-server (C/S) architecture, struggle to adapt to the new demands and changes in the field of architectural informatization. The development of a comprehensive BIM visualization system, which is web-based, requires no installation or downloading, is cross-platform, and facilitates easy sharing, has become a new pathway for BIM advancement. However, when rendering the vast amount of data in BIM three-dimensional scenes on the web, several issues persist, including low transmission efficiency, slow loading speed, laggy screen display, and low rendering frame rates, particularly on mobile devices and terminals with limited hardware performance. Therefore, this paper proposes BIM lightweighting techniques based on geometric data and texture information, as well as a web-based rendering optimization method for three-dimensional models. In terms of BIM lightweighting, more efficient methods and algorithms are employed to simultaneously lighten the geometric data and texture information of the models. For rendering optimization, an efficient view frustum culling algorithm is introduced, along with a design for an adaptive rendering strategy to enhance rendering performance.Through testing the loading efficiency of different scale BIM models before and after optimization was tested on the web. Results show that, while maintaining display accuracy, the average loading time of the optimized models was reduced by 40% compared to the unoptimized models. The average data compression rate reached 45%, and the average memory usage decreased by 32.55%. After stable rendering, the frame rate was close to 60fps.

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Surface polygonization of 3D objects using norm similarity

Saha

Biswas

2021

J Comb Optim

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How many sample points are sufficient for 3D model surface representation and accurate mesh simplification?

Cited by 7 publications

References 36 publications

3D Cartographic Generalization of LiDAR Point Clouds Based on the Principle of Self-Similarity of a Deterministic Fractal Structure

3D Cartographic Generalization of LiDAR Point Clouds Based on the Principle of Self-Similarity of a Deterministic Fractal Structure

Research on Lightweighting and Rendering Optimization Techniques for Building Information Models

Surface polygonization of 3D objects using norm similarity

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