VTK consists of a C++class library and several interpreted interface layers including Tcl/Tk, Java, and Python. Kitware, whose team created and continues to extend the toolkit, offers professional support and consulting services for VTK. VTK supports a wide variety of visualization algorithms including: scalar, vector, tensor, texture, and volumetric methods; and advanced modeling techniques such as: implicit modeling, polygon reduction, mesh smoothing, cutting, contouring, and Delaunay triangulation. VTK has an extensive information visualization framework, has a suite of 3D interaction widgets, supports parallel processing, and integrates with various databases and GUI toolkits such as Qt and Tk. VTK is cross-platform and runs on Linux, Windows, Mac and Unix platforms. At its core VTK is implemented as a C++ toolkit, requiring users to build applications by combining various objects into an application. The system also supports automated wrapping of the C++ core into Python, Java and Tcl, so that VTK applications may also be written using these programming languages.
History
VTK was initially created in 1993 as companion software to the book The Visualization Toolkit: An Object-Oriented Approach to 3D Graphics. The book and software were written by three researchers on their own time and with permission from GE. After the core of VTK was written, users and developers around the world began to improve and apply the system to real-world problems. With the founding of Kitware, the VTK community grew rapidly, and toolkit usage expanded into academic, research and commercial applications. A number of major companies and organizations, such as Sandia National Laboratories, Livermore National Laboratory, Los Alamos National Laboratory funded the development of VTK and even developed a number of VTK modules themselves. VTK forms the core of the 3DSlicer biomedical computing application, and numerous research papers at IEEE Visualization and other conferences based on VTK have appeared. VTK has been used on a large 1024-processor computer at the Los AlamosNational Laboratory to process nearly a Petabyte of data. Later VTK was expanded to support the ingestion, processing and display of informatics data. This work was supported by Sandia National Laboratories under the 'Titan' project.
Criticism
In 2013, a survey paper on visualization for radiotherapy noticed that while VTK is a powerful and widely known toolkit, it lacked a number of important features, such as multivolume rendering, had no support of GPGPU libraries such as CUDA, no support of out-of-core rendering of huge datasets and no native support for visualization of time-dependent volumetric data. Nonetheless, since 2013 there have been improvements such as VTK-m which can speed-up and parallelize certain computationally intensive tasks task using “accelerators” such as GPGPU. VTK is also used in the visualization pipeline of radiological imaging software such as MEDInria or Starviewer which perform multi-volume and time-dependent visualizations.
Applications
VTK is the basis of many visualization applications:
