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G Augmented-Reality by Serenial - Toolkit for LabVIEW Download

A Toolkit to create webcam-based Augmented-Reality Applications with LabVIEW

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Version	1.3.0.8
Released	Dec 13, 2025
Publisher	Serenial
License	Zero-Clause BSD
LabVIEW Version	LabVIEW>=20.0
Operating System	Windows NT,Linux
Dependencies	serenial_io_g_augmented_reality_toolkit_shared_binaries_ar serenial_io_g_augmented_reality_toolkit_shared_binaries_core serenial_io_g_augmented_reality_toolkit_shared_binaries_dnn serenial_io_g_augmented_reality_toolkit_shared_binaries_fd serenial_io_g_augmented_reality_toolkit_shared_binaries_opkg serenial_io_g_augmented_reality_toolkit_shared_binaries_tesseract serenial_io_g_augmented_reality_toolkit_shared_binaries_videoio
Project links	Homepage Documentation Repository Discussion

Description

Stop just using your webcam for video calls and start using it for Augmented Reality applications.

This toolkit brings a range of camera, image processing, and computer vision functionality into LabVIEW by leveraging open-source libraries, lovingly wrapped for your favourite graphical programming language.

A suite of tools for developing computer vision applications is provided, including

* A system-native camera driver built with Windows Media Foundation/Video4Linux system APIs
* Image manipulation and file I/O
* Video file handling in many common formats via FFmpeg
* Routines for detection of image-features including contours/shapes, text, barcode/QR codes and neural network driven classification thanks to integration of OpenCV with its DNN module, Tesseract OCR and ZXing
* Augmented Reality functionality such as camera calibration, image rectification, homography, pose estimation and mapping and localization as well as support for Apriltags
* 3D rendering utilites which integrate with the LabVIEW 3D picture control to provide GPU acceleration
* Interoperability with NI's IMAQ Image-Type and LabVIEW's Python node without any dependency on either

The BSD-zero LabVIEW code and dynamically linked LGPL-2.1 code make this toolkit compatible with a range of open-source and closed-source deployments and the shared binaries include all non-operating-system dependencies to simplify usage.

This toolkit supports Windows and Linux Desktop, NI-Linux-RT (x64) targets. The Core, AR and most non-DNN Feature Detection is also runnable on NI-Linux-RT (ARM32) and NI's HobbyToolkit/LINX RaspberryPi and BeagleBone Black targets.