VTK大数据集操作.docx

How to handle large data sets in VTK One of the challenges in VTK is to efficiently handle large datasets. By default VTK is tuned towards smaller datasets. For large datasets there are a couple of changes you can make that should yield a much smaller memory footprint (less swapping) and also improve rendering performance. The solution is to: Use ReleaseDataFlag, Turn on ImmediateModeRendering Use triangle strips via vtkStripper Use a different filter or mapper Each of these will be discussed below. Using ReleaseDataFlag By default VTK keeps a copy of all intermediate results between filters in a pipeline. For a pipeline with five filters this can result in having six copies of the data in memory at once. This can be controlled using ReleaseDataFlag and GlobalReleaseDataFlag. If ReleaseDataFlag is set to one on a data object, then once a filter has finished using that data object, it will release its memory. Likewise, if GlobalReleaseDataFlag is set on ANY data object, all data objects will release their memory once their dependent filter has finished executing. For example in Tcl and C++ # Tcl vtkPolyDataReader reader [reader GetOutput] ReleaseDataFlagOn // C++ vtkPolyDataReader *reader = vtkPolyDataReader::New(); reader-GetOutput()-ReleaseDataFlagOn();or // C++ vtkPolyDataReader *reader = vtkPolyDataReader::New(); reader-GetOutput()-GlobalReleaseDataFlagOn();While turning on the ReleaseDataFlag will reduce your memory footprint, the disadvantage is that none of the intermediate results are kept in memory. So if you interactively change a parameter of a filter (such as the isosurface value), all the filters will have to re-execute to produce the new result. When the intermediate results are stored in memory, only the downstream filters would have to re-execute. One hint for good interactive performance. If only one stage of the pipeline can have its parameters changed interactively (such as the target reduction in a decimation filter), only retain the data just p