Open source in virtual reality and augmented reality

Virtual reality is one of the key technologies that goes way beyond gaming into fields like nuclear reaction simulation, flight/battlefield simulation, and phobia treatment through the use of virtual models, etc. A few noteworthy open source tools are OSVR, ARToolkit, OpenSimulator, etc. OSVR or Open Source Virtual Reality ( http://www.osvr.org/) is an open source project initiated and sponsored by Sensics and Razer. It is provided under the Apache 2 licence. The main components include open source hardware (HMD or head mounted display) and an OSVR framework, which is open source software that provides a standardised way of discovering and configuring various VR and AR peripherals. ARToolKit ( http://artoolkit.org/) is an open source C library that aims at developing augmented reality applications. The open source project has its source code hosted over GitHub. It’s a cross-platform tool with support for Windows, Mac, Linux, iOS and Android platforms and hence is easily portable. The software makes use of various types of computer vision for robust feature tracking. OpenSim ( http://opensimulator.org/) is an open source multiplatform application server (mainly written in C#), which is primarily used for simulating virtual environments. The source code is released under a BSD licence, such that it can be easily used in projects. Still under development, this .NET based framework allows many features and an inheritance based approach that makes it highly extensible. TensorFlow from Google and DMTK (Distributed Machine Learning Toolkit) from Microsoft. Tensor Flow ( https://www.tensorflow.org/) is a numerical library mostly used for deep neural network based computation, where all the computations are modelled as data flow graphs. Recently outsourced by Google, the main advantages of this tool are its Python based API and easyto-use C++ interface, and its flexibility to run on a variety of hardware, ranging from cheap commodity hardware to heterogeneous GPU based systems. DMTK ( http://www.dmtk.io/) is an open source machine learning toolkit developed by Microsoft researchers. The toolkit provides a framework that can be used to train machine learning models on Big Data. Following a clientserver based model, this toolkit also provides two interprocess communication libraries, MPI and ZMQ, to handle a variety of cluster environments used for model training. Scikit-learn ( http://scikit-learn.org/) is a Python based tool with very active community support. The underlying compiled libraries NumPy, Scipy, Matplotlib, etc, make the tool fast and efficient. It is used for both academic as well as commercial ML development due to its versatile and simple APIs.