2018 Fiscal Year Annual Research Report
Acquisition, Restoration and Compression of 3D Geometric Data
| Project/Area Number |
18K11385
|
|---|
| Research Institution | National Institute of Informatics |
|---|
Principal Investigator |
CHEUNG GENE 国立情報学研究所, コンテンツ科学研究系, 准教授 (40577467)
|
|---|
| Project Period (FY) |
2018-04-01 – 2019-03-31
|
| Keywords | Point Cloud Denoising |
|---|
| Outline of Annual Research Achievements |
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.
|
Research Products
(1 results)
