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Extracting Surface Curvature from Noisy Scan Data
| Content Provider | Semantic Scholar |
|---|---|
| Author | Rugis, John |
| Copyright Year | 2006 |
| Abstract | In general, the noise that is present in real-world 3D surface scan data prevents accurate curvature calculation. In this paper we show how curvature can be extracted from noisy data by applying filtering after a noisy curvature calculation. To this end, we extend the standard Gaussian filter (as used in 2D image processing) by taking adjacent point distances along the scanned surface into account. A brief comparison is made between this new 2.5D Gaussian filter and a standard 2D Gaussian filter using data from the Digital Michelangelo Project. |
| File Format | PDF HTM / HTML |
| Alternate Webpage(s) | http://www.citr.auckland.ac.nz/~john-rugis/pdf/ivcnz06.pdf |
| Alternate Webpage(s) | http://www.citr.auckland.ac.nz/~john-rugis/pdf/291106_ivcnz.pdf |
| Alternate Webpage(s) | https://www.cs.auckland.ac.nz/~john-rugis/pdf/ivcnz06.pdf |
| Language | English |
| Access Restriction | Open |
| Content Type | Text |
| Resource Type | Article |
