ARToolKit Feature Comparison

ARToolKit v2.x and ARToolKit v5.x, while sharing a small subset of features, are vastly different. The latter represents nearly 8 years of further development of the former.

As well as obvious feature differences, the changes cover a wide variety of less obvious areas, including fundamental algorithms, internal design (modularity, reuse), optimization, external API design, connections to third-party systems, documentation and developer experience

Natural Feature Tracking

Natural feature tracking is a major feature present in ARToolKit v5.x that is not present in v2.x

  • Patented high-speed multi-resolution template-based tracker (libAR2)
  • Feature-detector based surface recognition and tracking initializer (libKPM)
  • Full suite of command-line tools, libraries, examples

Tracking

  • ICP pose estimator (vs. heuristic pose estimator in v2.x) with similar accuracy but 100x speed improvement.
  • Variable square marker borders
  • Variable square pictorial marker (template) resolution
  • 2D barcode marker support
  • Error detection and correction in barcode markers (BCH coding)
  • Automatic binarization threshold selection for square tracking
  • Pose estimate optimization using non-linear refinement
  • Robust pose estimator using M-estimation
  • Robust pose estimation from multi-square markers
  • Pose filtering

Tools Support

  • Simple camera calibration based on OpenCV
  • Web-based tools for barcode and NFT marker generation
  • On-device camera calibration app for Android which feeds into a distributed camera calibration database
  • Cloud-based distributed camera calibration database
  • On-device optical/stereo-optical calibration app for Android
  • New tools for square marker testing

Stereo and Optical See-Through Support

  • Support for simultaneous tracking from multiple video sources, e.g. stereo cameras
  • Stereo camera calibration
  • Robust pose estimation from calibrated stereo camera pairs
  • Stereo rendering support
  • Support for optical and stereo optical see-through displays on all platforms

Video Input Focus

  • Modular video input system (multiple video sources per platform, able to be selected at runtime)
  • iOS video support
  • Windows Media Foundation support
  • Windows DirectShow support
  • Windows FlyCapture SDK support (for Point Grey cameras)
  • Windows DVCam support
  • Windows QuickTime file/streaming support
  • OS X QTKit support
  • OS X QuickTime video file/streaming support
  • JPEG sequence input module (e.g. from M-JPEG stream, or high-resolution images) support
  • Linux/OSX lib1394 input support
  • Android video support
  • Support for high-resolution still-image capture during live tracking on iOS

Mobile Focus

  • Mobile-optimized (register size, memory usage)
  • OpenGL ES and ES 2.x support
  • Multi-platform mobile support
  • Automatic provision of camera calibration for Apple iOS devices.
  • Automatic provision of camera calibration data for Android devices via distributed camera calibration system
  • Integration with GPS and compass (iOS)

Optimization and Internals

  • Full 64-bit support
  • User-selectable floating point precision
  • Hand-tuned ARM assembly in performance critical sections
  • Optimized pathway for YUV video streams
  • Multithreading used throughout

New Languages and APIs

  • C++
  • Java (Android)
  • Objective C (iOS, OS X)
  • C#

Graphics and Rendering

  • Full support on all platforms for Unity 3D
  • Full OpenSceneGraph support for advanced rendering
  • Rendering of video from file or stream in-scene
  • Support for chroma-keying of video streams

Developer Experience

  • Full support for latest developer environments, including Xcode 6.x for iOS and OS X, Visual Studio 2013 for Windows, and Eclipse for Android
  • Vastly improved documentation, including new and improved reference documentation for over 350 API calls, as well as detailed guides and tutorials

Last modified: 2016/02/15 05:41 (external edit)

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