الفهرس 
ACKNOWLEDGEMENTOnly 14 pages are availabe for public view
 |  | from | 121 |  |  |
| | | from | 121 | | |
Abstract
Reality capture technologies such as laser scanner are becoming more democratize day-by-day, as such we are currently undergoing an influx of high-resolution photo-realistic 3D models that contain ever-increasing geometric and texture details and resolution. The massive data file size required to store this information can cause difficulty for both the transfer and manipulation of the captured model.
Both laser scanner and photogrammetry create huge point clouds. These point clouds have to be reduced wisely to enable meshes generation. Reduction algorithms are used to achieve point clouds with reasonable number of points correctly covers all sites features. The meaning here of the correct points coverage is to be dense at curved regions and distant at plane ones. These algorithms can be classified into three methods uniform, grid and curvature reduction. Curvature reduction method is the most accurate but it is time consuming.
In this thesis a new Rapid Curvature based Data Reduction (RCDR) algorithm is developed to reduce large point clouds rapidly and accurately. To validate the (RCDR) algorithm. The algorithm was coded by using C#. The comparison between the developed algorithm and the curvature algorithm shows a 5% reduction in accuracy and a huge amount of time saving in the RCDR algorithm. The point cloud,
Abstract
2
that takes about 36 hours processing time using the curvature algorithm, takes only about 11 minutes using the developed one. So, one can fairly see the efficiency of the developed algorithm.