Acquisition, Restoration and Compression of 3D Geometric Data

3D 几何数据的采集、恢复和压缩

• 批准号: 18K11385
• 负责人: CHEUNG GENE
• 金额: $ 2.16万
• 依托单位: National Institute of Informatics
• 依托单位国家: 日本
• 项目类别: Grant-in-Aid for Scientific Research (C)
• 财政年份: 2018
• 资助国家: 日本
• 起止时间: 2018-04-01 至 2019-03-31
• 项目状态: 已结题
• 来源: https://kaken.nii.ac.jp/en/grant/KAKENHI-PROJECT-18K11385/
• 关键词: Point Cloud Denoising; ３次元画像処理; グラフ信号処理

The focus of this research period is in the denoising of 3D point cloud. A point cloud is a collection of non-uniform discrete samples of 3D geometry of a physical object, such as human body.Leveraging on recent advances in graph signal processing, in our approach we design a graph-based regularization term called reweighted graph Laplacian regularization (RGLR) to regularize an otherwise ill-posed inverse problem. RGLR has a number of desirable properties, including: i) rotation-invariant, ii) promotion of piecewise-smoothness, and iii) fast optimization, where the RGLR can be computed efficient via iterative quadratic programming. Experimental results show that compared to existing point cloud denoising schemes, our proposed RGLR-based scheme has better performance at lower complexity.

本期研究的重点是3D点云的去噪。点云是物理对象（例如人体）3D 几何形状的非均匀离散样本的集合。利用图信号处理的最新进展，在我们的方法中，我们设计了一种基于图的正则化项，称为重新加权图拉普拉斯正则化（RGLR）来正则化一个不适定的逆问题。 RGLR 具有许多理想的特性，包括：i) 旋转不变，ii) 提高分段平滑性，以及 iii) 快速优化，其中 RGLR 可以通过迭代二次规划高效计算。实验结果表明，与现有的点云去噪方案相比，我们提出的基于RGLR的方案以较低的复杂度具有更好的性能。

项目成果

Iterative Graph-Based Filtering for Image Abstraction and Stylization

用于图像抽象和风格化的基于迭代图的过滤

• DOI: 10.1109/tcsii.2017.2669866
• 发表时间: 2018-02-01
• 期刊: IEEE Transactions on Circuits and Systems II: Express Briefs
• 作者: H. Sadreazami;A. Asif;Arash Mohammadi
• 通讯作者: Arash Mohammadi