Abstract
This article addresses the problem of denoising 3D data from LIDAR. It is a step often required to allow a good reconstruction of surfaces represented by point clouds. In this paper, we present an original algorithm inspired by a recent method developed by (Buades and Morel, 2005) in the field of image processing, the Non Local Denoising (NLD). With a local geometric descriptor, we look for points that have similarities in order to reduce noise while preserving the surface details. We describe local geometry by MLS surfaces and we use a local reference frame invariant by rotation for denoising points. We present our results on synthetic and real data.
| Original language | English |
|---|---|
| Pages (from-to) | 109-114 |
| Number of pages | 6 |
| Journal | International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences - ISPRS Archives |
| Volume | 38 |
| Publication status | Published - 1 Jan 2010 |
| Externally published | Yes |
| Event | ISPRS Technical Commission III Symposium on Photogrammetric Computer Vision and Image Analysis, PCV 2010 - Saint-Mande, France Duration: 1 Sept 2010 → 3 Sept 2010 |
Keywords
- Denoising
- Descriptor
- LIDAR
- Mesh
- Point Cloud