Rolling guidance normal filter for geometric processing

Wang, Peng-Shuai; Fu, Xiao‐Ming; Liu, Yang; Tong, Xin; Shi-lin, Liu; Guo, Baining

doi:10.1145/2816795.2818068

Cited by 58 publications

(56 citation statements)

References 43 publications

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“…Based on image texture filtering, some studies about the texture filtering [WFL*15, CHR*19] in 3D domain have been developed. For example, inspired by the bilateral texture filtering [CLKL14], Zhang et al [ZDZ*15] proposed a framework of guided mesh normal filtering.…”

Section: Related Workmentioning

confidence: 99%

“…A reliable guidance normal field is presented to imply the surface features in the process of the joint bilateral filtering. Similarly, Wang et al [WFL*15] extended the image filtering work [ZSXJ14] to geometric processing. It is noticeable that the rolling guidance normal filter (RGNF) is also a two‐stage method and is able to deal with multi‐scale features.…”

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%

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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: Related Workmentioning

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%

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Reliable Rolling‐guided Point Normal Filtering for Surface Texture Removal

Sun

Chen

Qin

et al. 2019

Computer Graphics Forum

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“…The resulting hyperspectral date cube enables precise material identification with the abundance spectral information, as each pixel can be represented by a spectral signature or fingerprint that characterizes the underling objects [1,2]. However, one of the challenges confronting hyperspectral remote sensing image processing is segmentation, saliency detection, and so on [37][38][39]. Differing from the previous spatial sparse unmixing methods, the proposed RGSU algorithm is scale-aware and can separate the different detail levels and achieve unmixing results with clear boundaries or texture information and clear background regions.…”

Section: Introductionmentioning

confidence: 99%

Rolling Guidance Based Scale-Aware Spatial Sparse Unmixing for Hyperspectral Remote Sensing Imagery

et al. 2017

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Abstract:Spatial regularization based sparse unmixing has attracted much attention in the hyperspectral remote sensing image processing field, which combines spatial information consideration with a sparse unmixing model, and has achieved improved fractional abundance results. However, the traditional spatial sparse unmixing approaches can suppress discrete wrong unmixing points and smooth an abundance map with low-contrast changes, and it has no concept of scale difference. In this paper, to better extract the different levels of spatial details, rolling guidance based scale-aware spatial sparse unmixing (namely, Rolling Guidance Sparse Unmixing (RGSU)) is proposed to extract and recover the different levels of important structures and details in the hyperspectral remote sensing image unmixing procedure, as the different levels of structures and edges in remote sensing imagery have different meanings and importance. Differing from the existing spatial regularization based sparse unmixing approaches, the proposed method considers the different levels of edges by combining a Gaussian filter-like method to realize small-scale structure removal with a joint bilateral filtering process to account for the spatial domain and range domain correlations. The proposed method is based on rolling guidance spatial regularization in a traditional spatial regularization sparse unmixing framework, and it accomplishes scale-aware sparse unmixing. The experimental results obtained with both simulated and real hyperspectral images show that the proposed method achieves visual effects better and produces higher quantitative results (i.e., higher SRE values) when compared to the current state-of-the-art sparse unmixing algorithms, which illustrates the effectiveness of the rolling guidance based scale aware method. In the future work, adaptive scale-aware spatial sparse unmixing framework will be studied and developed to enhance the current idea.

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Fast and faithful scale-aware image filters

Yoshizawa

Yokota

2021

Vis Comput

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This paper proposes a fast and accurate computational framework for scale-aware image filters. Our framework is based on accurately approximating $$L^{1}$$ L 1 Gaussian convolution with respect to a transformed pixel domain representing geodesic distance on a guidance image manifold in order to recover salient edges in a manner faithful to scale-space theory while removing small image structures. Our framework possesses linear computational complexity with high approximation precision. We examined it numerically in terms of speed, accuracy, and quality compared with conventional methods.

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Rolling guidance normal filter for geometric processing

Cited by 58 publications

References 43 publications

Reliable Rolling‐guided Point Normal Filtering for Surface Texture Removal

Reliable Rolling‐guided Point Normal Filtering for Surface Texture Removal

Rolling Guidance Based Scale-Aware Spatial Sparse Unmixing for Hyperspectral Remote Sensing Imagery

Fast and faithful scale-aware image filters

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