Abstract
In this paper, a new method for computing surface parameters, especially the surface roughness, is presented. This method is designed for easily reconstruct and extract informations from a collection of photos taken without any constraints. This absence of constraints is possible since camera calibration can be computed with bundle adjustment auto-calibration methods. 3D information can then be retrieved with triangulation techniques from the disparity maps computed for each image pair. This paper proposes a new statistically grounded extraction of the roughness directly on the 3D point cloud. Joining 3D and image processing methods, the roughness can be computed only on certain objects with image segmentation. The results are shown on different datasets proving the method robustness.
| Original language | English |
|---|---|
| Pages (from-to) | 104-109 |
| 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
- Auto-calibration
- Bundle adjustment
- Epipolar geometry
- Point cloud
- Soil roughness
- Stereo-vision