عملية المعالجة المسبقة لمجموعة البيانات ثلاثية الأبعاد للأماكن الداخلية والخارجية

الملخص

Abstract:

       This paper  interested with  3D data forms , 3D point cloud are introduced strong realistic description of big different 3D scenes and object , point cloud is extremely high owing jam caused by huge quantity of data generated from laser scanning device, noise points will be appear in scanning devices by factors of environments. All these noise and unwanted data can be very affected modeling. The point cloud preprocessing is analyzing and changing points cloud to optimize its store , transportation and quality using different computational and mathematical algorithm like compression point cloud, which aid to decrease the storage space or cost and time of transmission,  Point cloud clustering aid to eliminate noise and outlier from three dimension points cloud, the point cloud downsample is aim to reduce the points number  and KDTree used for structure point cloud in efficient order to find nearest neighbors points, The software process 29 million points while still enabling the user to visualize and navigation  smoothly through the point cloud  This research working on semantic3D outdoor dataset and Stanfored3D indoor dataset,  programming with python 3.7, cloud compare  free software v2.6.3 and computer with core i7MP , RAM 16 , NVidia RTX 2060. 

Keywords: 3D point cloud, Kdtree algorithm, downsample operation, DB scan cluster algorithm, Remove hidden point’s algorithm, Open3 library.

Note: the research is based on a doctoral thesis.

• Introduction