Patch-based mesh inpainting via low rank recovery

IF 2.5 4区计算机科学 Q2 COMPUTER SCIENCE, SOFTWARE ENGINEERING

Graphical Models Pub Date : 2022-07-01 DOI:10.1016/j.gmod.2022.101139

Xiaoqun Wu, Xiaoyun Lin, Nan Li, Haisheng Li

引用次数: 2

Abstract

Mesh inpainting aims to fill the holes or missing regions from observed incomplete meshes and keep consistent with prior knowledge. Inspired by the success of low rank in describing similarity, we formulate the mesh inpainting problem as the low rank matrix recovery problem and present a patch-based mesh inpainting algorithm. Normal patch covariance is adapted to describe the similarity between surface patches. By analyzing the similarity of patches, the most similar patches are packed into a matrix with low rank structure. An iterative diffusion strategy is first designed to recover the patch vertex normals gradually. Then, the normals are refined by low rank approximation to keep the overall consistency and vertex positions are finally updated. We conduct several experiments in different 3D models to verify the proposed approach. Compared with existing algorithms, our experimental results demonstrate the superiority of our approach both visually and quantitatively in recovering the mesh with self-similarity patterns.

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基于补丁的网格绘制通过低等级恢复

网格补绘的目的是填补观察到的不完整网格中的空洞或缺失区域，并保持与先验知识的一致性。受低秩描述相似度成功的启发，我们将网格绘制问题表述为低秩矩阵恢复问题，并提出了一种基于patch的网格绘制算法。采用正态斑块协方差来描述表面斑块之间的相似性。通过分析patch的相似度，将最相似的patch打包成一个低阶结构的矩阵。首先设计迭代扩散策略，逐步恢复patch顶点法线;然后，通过低秩近似法对法线进行细化以保持整体一致性，最后更新顶点位置。我们在不同的3D模型中进行了几个实验来验证所提出的方法。与现有算法相比，我们的实验结果表明，我们的方法在视觉上和定量上都具有自相似模式的网格恢复的优势。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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来源期刊

Graphical Models 工程技术-计算机：软件工程

CiteScore

3.60

自引率

5.90%

发文量

审稿时长

47 days

期刊介绍： Graphical Models is recognized internationally as a highly rated, top tier journal and is focused on the creation, geometric processing, animation, and visualization of graphical models and on their applications in engineering, science, culture, and entertainment. GMOD provides its readers with thoroughly reviewed and carefully selected papers that disseminate exciting innovations, that teach rigorous theoretical foundations, that propose robust and efficient solutions, or that describe ambitious systems or applications in a variety of topics. We invite papers in five categories: research (contributions of novel theoretical or practical approaches or solutions), survey (opinionated views of the state-of-the-art and challenges in a specific topic), system (the architecture and implementation details of an innovative architecture for a complete system that supports model/animation design, acquisition, analysis, visualization?), application (description of a novel application of know techniques and evaluation of its impact), or lecture (an elegant and inspiring perspective on previously published results that clarifies them and teaches them in a new way). GMOD offers its authors an accelerated review, feedback from experts in the field, immediate online publication of accepted papers, no restriction on color and length (when justified by the content) in the online version, and a broad promotion of published papers. A prestigious group of editors selected from among the premier international researchers in their fields oversees the review process.