Mesh Denoising Based on Normal Voting Tensor and Binary Optimization

Yadav, Sunil Kumar; Reitebuch, Ulrich; Polthier, Konrad

doi:10.1109/tvcg.2017.2740384

Cited by 51 publications

(61 citation statements)

References 36 publications

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“…In last two decades, a wide variety of smoothing algorithms have been introduced to remove undesired noise, while preserving sharp features in the geometry. For a comprehensive review on mesh denoising, readers are referred to [1], [2] and [3]. Here, an overview of current state-ofthe-art methods is given, which are based on differential coordinates and bilateral filtering.…”

Section: Related Workmentioning

confidence: 99%

“…Later, researchers utilized the L 1median normal filtering along with vertex preprocessing to produce a noise free surface [28]. Recently, Yadav et al [3] proposed a binary optimization-based mesh denoising algorithm, where noise was removed by assigning a binary values to a face normal-based covariance matrix. Centin et al [2] proposed a normal-diffusion-based mesh denoising algorithm, which removes noise components without tempering the metric quality of a surface.…”

Section: Related Workmentioning

confidence: 99%

“…where n j represents a noisy face normal and belongs to the geometric neighbour disk Ω i of the central face normal n i . The geometric neighborhood is more robust against nonuniform meshes compared to the combinatorial neighborhood [3]. The normalization factor K i is defined as:…”

Section: Robust Bilateral Face Normal Processingmentioning

confidence: 99%

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Robust and High Fidelity Mesh Denoising

Yadav

Reitebuch

Polthier

2019

IEEE Trans. Visual. Comput. Graphics

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This paper presents a simple and effective two-stage mesh denoising algorithm, where in the first stage, face normal filtering is done by using bilateral normal filtering in a robust statistics framework. Tukey's bi-weight function is used as similarity function in the bilateral weighting, which is a robust estimator and stops the diffusion at sharp edges to retain features and removes noise from flat regions effectively. In the second stage, an edge-weighted Laplace operator is introduced to compute a differential coordinate. This differential coordinate helps the algorithm to produce a high-quality mesh without any face normal flips and makes the method robust against high-intensity noise.

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Section: Related Workmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

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Robust and High Fidelity Mesh Denoising

Yadav

Reitebuch

Polthier

2019

IEEE Trans. Visual. Comput. Graphics

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“…Bilateral filtering is initially used in image denoising [50], and it is successfully extended to mesh denoising. Regarding the feature-preserving techniques, the utilization of filtered normals has seen noticeable progress in recent years [37,38,40,41,42,43,44,45,46,35,47,48,43,49,14,15,51,52]. Some recent methods have focused on vertex and face classification before mesh denoising [9,10,11,12,13].…”

Section: Related Workmentioning

confidence: 99%

HLO: Half-kernel Laplacian operator for surface smoothing

Pan

Gong

et al. 2020

Computer-Aided Design

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This paper presents a simple yet effective method for feature-preserving surface smoothing. Through analyzing the differential property of surfaces, we show that the conventional discrete Laplacian operator with uniform weights is not applicable to feature points at which the surface is non-differentiable and the second order derivatives do not exist. To overcome this difficulty, we propose a Half-kernel Laplacian Operator (HLO) as an alternative to the conventional Laplacian. Given a vertex v, HLO first finds all pairs of its neighboring vertices and divides each pair into two subsets (called half windows); then computes the uniform Laplacians of all such subsets and subsequently projects the computed Laplacians to the full-window uniform Laplacian to alleviate flipping and degeneration. The half window with least regularization energy is then chosen for v. We develop an iterative approach to apply HLO for surface denoising. Our method is conceptually simple and easy to use because it has a single parameter, i.e., the number of iterations for updating vertices. We show that our method can preserve features better than the popular uniform Laplacian-based denoising and it significantly alleviates the shrinkage artifact. Extensive experimental results demonstrate that HLO is better than or comparable to state-of-the-art techniques both qualitatively and quantitatively and that it is particularly good at handling meshes with high noise. We will make our source code publicly available.

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“…Yadav et al . [YRP17] propose an iterative denoising method that successively processes normals and vertex positions, and introduce the concept of element‐based normal voting tensor for smoothing. Most of these methods target offline high‐quality denoising and are not usable in practice for interactive application scenarios.…”

Section: Introductionmentioning

confidence: 99%

Filtered Quadrics for High‐Speed Geometry Smoothing and Clustering

Legrand

Thiery

Boubekeur

2019

Computer Graphics Forum

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Modern 3D capture pipelines produce dense surface meshes at high speed, which challenge geometric operators to process such massive data on‐the‐fly. In particular, aiming at instantaneous feature‐preserving smoothing and clustering disqualifies global variational optimizers and one usually relies on high‐performance parallel kernels based on simple measures performed on the positions and normal vectors associated with the surface vertices. Although these operators are effective on small supports, they fail at properly capturing larger scale surface structures. To cope with this problem, we propose to enrich the surface representation with filtered quadrics, a compact and discriminating range space to guide processing. Compared to normal‐based approaches, this additional vertex attribute significantly improves feature preservation for fast bilateral filtering and mode‐seeking clustering, while exhibiting a linear memory cost in the number of vertices and retaining the simplicity of convolutional filters. In particular, the overall performance of our approach stems from its natural compatibility with modern fine‐grained parallel computing architectures such as graphics processor units (GPU). As a result, filtered quadrics offer a superior ability to handle a broad spectrum of frequencies and preserve large salient structures, delivering meshes on‐the‐fly for interactive and streaming applications, as well as quickly processing large data collections, instrumental in learning‐based geometry analysis.

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Mesh Denoising Based on Normal Voting Tensor and Binary Optimization

Cited by 51 publications

References 36 publications

Robust and High Fidelity Mesh Denoising

Robust and High Fidelity Mesh Denoising

HLO: Half-kernel Laplacian operator for surface smoothing

Filtered Quadrics for High‐Speed Geometry Smoothing and Clustering

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