Constraint-based point set denoising using normal voting tensor and restricted quadratic error metrics
Autor: | Ulrich Reitebuch, Eric Zimmermann, Martin Skrodzki, Konrad Polthier, Sunil Kumar Yadav |
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Rok vydání: | 2018 |
Předmět: |
Computer science
Covariance matrix General Engineering 020207 software engineering 02 engineering and technology Computer Graphics and Computer-Aided Design Vertex (geometry) Human-Computer Interaction Feature (computer vision) Outlier Principal component analysis Metric (mathematics) 0202 electrical engineering electronic engineering information engineering 020201 artificial intelligence & image processing Tensor Algorithm Eigenvalues and eigenvectors ComputingMethodologies_COMPUTERGRAPHICS |
Zdroj: | Computers & Graphics. 74:234-243 |
ISSN: | 0097-8493 |
Popis: | In many applications, point set surfaces are acquired by 3D scanners. During this acquisition process, noise and outliers are inevitable. For a high fidelity surface reconstruction from a noisy point set, a feature preserving point set denoising operation has to be performed to remove noise and outliers from the input point set. To suppress these undesired components while preserving features, we introduce an anisotropic point set denoising algorithm in the normal voting tensor framework. The proposed method consists of three different stages that are iteratively applied to the input: in the first stage, noisy vertex normals, are initially computed using principal component analysis, are processed using a vertex-based normal voting tensor and binary eigenvalues optimization. In the second stage, feature points are categorized into corners, edges, and surface patches using a weighted covariance matrix, which is computed based on the processed vertex normals. In the last stage, vertex positions are updated according to the processed vertex normals using restricted quadratic error metrics. For the vertex updates, we add different constraints to the quadratic error metric based on feature (edges and corners) and non-feature (planar) vertices. Finally, we show our method to be robust and comparable to state-of-the-art methods in several experiments. |
Databáze: | OpenAIRE |
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