Rolling normal filtering for point clouds

Zheng, Yinglong; Li, Guiqing; Xu, Xuemiao; Wu, Shihao; Nie, Yongwei

doi:10.1016/j.cagd.2018.03.004

Cited by 32 publications

(30 citation statements)

References 24 publications

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“…However, there are few approaches that specialize in texture removal. Fortunately, the rolling normal filter [ZLX*18] which aims at removing surface textures can be compared. It can be seen from Fig.…”

Section: Results and Analysismentioning

confidence: 99%

“…17 has shown. In detail, RIMLS and the rolling normal filter of [ZLX*18] are able to remove textures with some residuals. However, the results of EAR do not change significantly compared with that of the inputs.…”

Section: Results and Analysismentioning

confidence: 99%

“…In the point cloud domain, there are at present relatively few studies on texture removal due to the complexity of 3D space and the lack of explicit topological information. Recently, Zheng et al [ZLX*18] designed a texture filtering method. They recover the normals of point cloud by first applying the rolling filter on the normal field and then updating point positions to match the filtered normal field.…”

Section: Related Workmentioning

confidence: 99%

“…Different from [ZLX*18, WFL*15], an improved Gaussian filter is devised to apply on the initial normal field:…”

Section: Texture Removalmentioning

confidence: 99%

“…Texture filtering has been studied extensively in image processing in past decades [CLKL14, TM98, XLXJ11, KEE13]. In particular, the rolling guidance filter presented by Zhang et al [ZSXJ14] and its counterparts (RGNF, rolling guidance normal filter) in the 3D field [ZLX*18, WFL*15] have achieved impressive results. These methods commonly first utilize the Gaussian filter to smooth the input image or 3D surface and then recover the over‐smoothed structures by a rolling guidance filter.…”

Section: Introductionmentioning

confidence: 99%

See 4 more Smart Citations

Reliable Rolling‐guided Point Normal Filtering for Surface Texture Removal

Sun

Chen

Qin

et al. 2019

Computer Graphics Forum

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Semantic surface decomposition (SSD) facilitates various geometry processing and product re‐design tasks. Filter‐based techniques are meaningful and widely used to achieve the SSD, which however often leads to surface either under‐fitting or over‐fitting. In this paper, we propose a reliable rolling‐guided point normal filtering method to decompose textures from a captured point cloud surface. Our method is built on the geometry assumption that 3D surfaces are comprised of an underlying shape (US) and a variety of bump ups and downs (BUDs) on the US. We have three core contributions. First, by considering the BUDs as surface textures, we present a RANSAC‐based sub‐neighborhood detection scheme to distinguish the US and the textures. Second, to better preserve the US (especially the prominent structures), we introduce a patch shift scheme to estimate the guidance normal for feeding the rolling‐guided filter. Third, we formulate a new position updating scheme to alleviate the common uneven distribution of points. Both visual and numerical experiments demonstrate that our method is comparable to state‐of‐the‐art methods in terms of the robustness of texture removal and the effectiveness of the underlying shape preservation.

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Section: Results and Analysismentioning

confidence: 99%

Section: Results and Analysismentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

“…Different from [ZLX*18, WFL*15], an improved Gaussian filter is devised to apply on the initial normal field:…”

Section: Texture Removalmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

Reliable Rolling‐guided Point Normal Filtering for Surface Texture Removal

Sun

Chen

Qin

et al. 2019

Computer Graphics Forum

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Surface Denoising Based on Normal Filtering in a Robust Statistics Framework

Yadav

Skrodzki

Zimmermann

et al. 2021

Proceedings of the Forum "Math-for-Industry" 2018

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During a surface acquisition process using 3D scanners, noise is inevitable and an important step in geometry processing is to remove these noise components from these surfaces (given as points-set or triangulated mesh). The noise-removal process (denoising) can be performed by filtering the surface normals first and by adjusting the vertex positions according to filtered normals afterwards. Therefore, in many available denoising algorithms, the computation of noise-free normals is a key factor. A variety of filters have been introduced for noise-removal from normals, with different focus points like robustness against outliers or large amplitude of noise. Although these filters are performing well in different aspects, a unified framework is missing to establish the relation between them and to provide a theoretical analysis beyond the performance of each method. In this paper, we introduce such a framework to establish relations between a number of widely-used nonlinear filters for face normals in mesh denoising and vertex normals in point set denoising. We cover robust statistical estimation with M-smoothers and their application to linear and non-linear normal filtering. Although these methods originate in different mathematical theories-which include diffusion-, bilateral-, and directional curvature-based algorithms-we demonstrate that all of them can be cast into a unified framework of robust statistics using robust error norms and their corresponding influence functions. This unification contributes to a better understanding of the individual methods and their relations with each other. Furthermore, the presented framework provides a platform for new techniques to combine the advantages of known filters and to compare them with available methods.

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