Search Results for "three.js"

This JavaScript library detects and tracks the face in real time from the camera video feed captured with WebRTC. Then it is possible to overlay 3D content for augmented reality applications. We provide various demonstrations using main WebGL 3D engines. We have included in this repository the release versions of the 3D engines to work with a determined version (they are in /libs/<name of the engine>/). This library is lightweight and it does not include any 3D engine or third-party library....

AR.js is a lightweight JavaScript library for delivering augmented reality experiences on the web. It supports image tracking, location-based AR, and marker-based interactions—all without requiring native applications or plugins. AR.js was among those great projects, with great technologies, often disruptive ones, that eventually were lacking from documentation and support. We tried to carry on the author's terrific work by maintaining this project, handling new versions, issues, pull...

Gio.js is an open source library for web 3D globe data visualization built with Three.js. What makes Gio.js different is that it is simple to use Gio.js to customize a 3D data visualization model in a declarative way, add your own data, and integrate it into your own modern web application. Gio.js is an open source library for web 3D globe data visualization built with Three.js. What makes Gio.js different is that it is simple to use Gio.js to customize a 3D data visualization model in a declarative way, add your own data, and integrate it into your own modern web application. ...

TensorSpace is a neural network 3D visualization framework built using TensorFlow.js, Three.js and Tween.js. TensorSpace provides Keras-like APIs to build deep learning layers, load pre-trained models, and generate a 3D visualization in the browser. From TensorSpace, it is intuitive to learn what the model structure is, how the model is trained and how the model predicts the results based on the intermediate information.

...The tool provides the ability to generate figures of three kinds: classic Fully-Connected Neural Network (FCNN) figures, Convolutional Neural Network (CNN) figures of the sort introduced in the LeNet paper, and Deep Neural Network figures following the style introduced in the AlexNet paper. The former two are accomplished using the D3 javascript library and the latter with the javascript library Three.js. NN-SVG provides the ability to style the figure to the user's liking via many size, color, and layout parameters.

egjs is Javascript components group that brings easiest and fastest way to build a web application in your way.