| 研究実績の概要 |
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.
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