Name
Abstract | ||
|---|---|---|
Semantic surface decomposition (SSD) facilitates various geometry processing and product re-design tasks. Filter-based techniques are meaningful and widely used to achieve the SSD, which however often leads to surface either under-fitting or over-fitting. In this paper, we propose a reliable rolling-guided point normal filtering method to decompose textures from a captured point cloud surface. Our method is built on the geometry assumption that 3D surfaces are comprised of an underlying shape (US) and a variety of bump ups and downs (BUDs) on the US. We have three core contributions. First, by considering the BUDs as surface textures, we present a RANSAC-based sub-neighborhood detection scheme to distinguish the US and the textures. Second, to better preserve the US (especially the prominent structures), we introduce a patch shift scheme to estimate the guidance normal for feeding the rolling-guided filter. Third, we formulate a new position updating scheme to alleviate the common uneven distribution of points. Both visual and numerical experiments demonstrate that our method is comparable to state-of-the-art methods in terms of the robustness of texture removal and the effectiveness of the underlying shape preservation. |
Year | DOI | Venue |
|---|---|---|
2019 | 10.1111/cgf.13874 | COMPUTER GRAPHICS FORUM |
Field | DocType | Volume |
Computer vision,Computing Methodologies,Computer science,Filter (signal processing),Artificial intelligence,Surface finish,Shape analysis (digital geometry) | Journal | 38.0 |
Issue | ISSN | Citations |
7.0 | 0167-7055 | 0 |
PageRank | References | Authors |
0.34 | 0 | 6 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Yangxing Sun | 1 | 0 | 0.34 |
| Honghua Chen | 2 | 6 | 2.76 |
| Jing Qin | 3 | 1109 | 95.43 |
| Hongwei Li | 4 | 0 | 0.34 |
| Mingqiang Wei | 5 | 125 | 22.66 |
| Hua Zong | 6 | 0 | 1.01 |