Awesome GitHub RepositoriesThree-Dimensional Visualizations

Apache ECharts is a JavaScript data visualization library used for rendering interactive charts and complex data visualizations in web browsers. It functions as a canvas-based charting engine and a statistical data visualization suite that transforms datasets into visual representations. The framework provides specialized capabilities for three-dimensional data visualization, including the generation of 3D plots and globe visualizations. It also serves as a web-based geographic mapping tool for overlaying heatmaps, routes, and data distributions onto interactive maps. The library covers a br

ECharts is a JavaScript data visualization library and web charting framework used to render interactive 2D and 3D data plots within a web browser. It functions as a visualization engine that transforms raw data into customizable charts and graphs. The project includes a WebGL-based hardware acceleration engine specifically for producing three-dimensional plots and globe visualizations. This allows the library to handle large and complex datasets through GPU-accelerated rendering. The framework supports both canvas-based raster rendering and SVG-based vector rendering. It provides capabiliti

Babylon.js is a JavaScript game engine and real-time graphics renderer designed for creating interactive three-dimensional visuals and applications. It functions as a web-based 3D framework and WebGL engine that enables the deployment of high-performance 3D content across various web platforms and devices. The project provides tools for web-based 3D game development, real-time graphics rendering, and the creation of browser-based interactive visualizations. It also supports the development of WebXR virtual and augmented reality experiences using standard web technologies. The framework cover

The Point Cloud Library is a collection of C++ algorithms designed for filtering, registering, and analyzing large-scale 3D spatial datasets. It provides a framework for 3D point cloud processing, incorporating tools for spatial data filtering and geometric feature estimation. The library includes specialized systems for aligning multiple spatial datasets into a single unified coordinate system and a rendering engine for the visual inspection and analysis of processed point cloud data. It also features tools for calculating spatial descriptors to identify structural patterns and shapes within

Visdom is a tool for scientific experiment tracking and real-time data monitoring. It provides a programmatic interface for broadcasting live plots, rich media, and training metrics from scripts to an interactive web dashboard. The project specializes in high-dimensional data analysis, offering capabilities to project complex feature sets into 2D space using t-SNE and visualize PyTorch model embeddings. It organizes visualizations into named environments, allowing users to isolate different experimental runs and compare plots across these environments in a single view. The system covers a br

This project is a collection of optional, community-contributed algorithms and specialized vision tools that extend the core OpenCV framework. It serves as a comprehensive library of extra modules for computer vision research, providing advanced toolsets for image processing, visual data analysis, and object detection. The library includes specialized frameworks for augmented reality tracking, biometric face recognition, and three-dimensional pose estimation. It provides distinct capabilities for identifying AR markers, tracking 3D object silhouettes, and performing neural network vulnerabili

Stellarium is an open-source 3D planetarium simulator and cross-platform sky map used for astronomical observation and study. It provides a real-time simulation of the night sky that renders astronomical objects and atmospheric conditions using OpenGL. The software functions as a visualization tool to show how stars and planets appear through optical instruments. It allows users to identify celestial objects from any location on Earth, simulate stargazing site visibility, and plan targets for observation with binoculars or telescopes. The system incorporates an atmospheric scattering model,

This project is a collection of PyTorch learning resources and educational guides designed to teach the construction and training of neural networks. It serves as a comprehensive deep learning tutorial covering various model architectures and practical implementation strategies. The resources provide specific guidance on implementing computer vision tasks, such as image classification and synthetic imagery generation, as well as reinforcement learning agents using value networks and experience replay. It also covers sequential data modeling through recurrent networks and generative modeling u

This project is a manifold learning and non-linear dimensionality reduction library used to project high-dimensional data into lower-dimensional spaces while preserving topological structure. It functions as a parametric embedding framework and a topological data visualization library for identifying clusters and patterns within complex datasets. The library distinguishes itself through parametric neural mapping, which uses neural networks to learn functional mappings that allow for out-of-sample projections and the reconstruction of original data. It supports supervised and semi-supervised d

Verba is a retrieval-augmented generation interface and chatbot that uses Weaviate to provide factual answers based on private datasets. It functions as a vector database knowledge base, combining a hybrid search engine with an orchestration interface to connect various large language model providers and embedding services. The system differentiates itself through a RAG pipeline manager for adjusting text chunking rules and retrieval settings, alongside a 3D vector space visualization tool for analyzing the spatial organization and clustering of high-dimensional embeddings. It employs a modul

This is a PyTorch-based computer vision library for detecting 2D and 3D facial landmark coordinates. It functions as a facial landmark detector and reconstruction tool, utilizing deep learning to identify precise geometric points on human faces from image datasets. The library allows for the selection of specific detection backends to balance accuracy and processing speed. It supports the integration of precomputed bounding box files, which enables the system to bypass the initial detection phase and proceed directly to landmark extraction. The toolkit includes capabilities for batch image p

DensePose is a 3D human pose estimation framework designed to map 2D image pixels to a 3D surface-based model of the human body in real time. It functions as a computer vision anatomical mapper that projects 2D visual data onto a 3D surface to create detailed anatomical representations. The system operates as an image-to-3D texture transfer engine, localizing 2D image annotations onto 3D models to apply photographic textures to digital human representations. It uses a surface-based body mapping method to associate human pixels in an RGB image with specific coordinates on a 3D body template.

NonEuclidean is a graphics framework and rendering engine designed to compute and display three-dimensional scenes using non-standard spatial rules and geometries. It serves as a visualization tool for exploring complex mathematical spaces where traditional Euclidean laws of distance and angle do not apply. The project implements custom rendering pipelines to visualize non-standard geometric projections and warped spatial logic. This includes the ability to map non-Euclidean coordinates to Poincaré disk or Klein models to render curved space on flat screens. The system utilizes dynamic metri

H3 is an open-source library that provides a hierarchical hexagonal grid system for geospatial indexing. It projects the Earth onto an icosahedron and tiles each face with hexagons to minimize distortion, then encodes each hexagon as a 64-bit integer that stores its resolution and position in the hierarchy. This integer encoding enables fast bitwise operations for grid navigation and spatial analysis. The library offers a comprehensive set of grid topology algorithms for computing neighbor relationships, distances, and paths between cells directly on the hexagonal grid without geographic coor

Ce projet est une bibliothèque de machine learning fournissant une collection d'implémentations pour des algorithmes d'apprentissage supervisé et non supervisé. Il sert de framework de deep learning, de collection de classificateurs statistiques et de suite d'outils pour l'apprentissage non supervisé et la réduction de dimensionnalité. La bibliothèque permet la construction de réseaux de neurones, incluant des perceptrons multicouches et des réseaux convolutifs pour la reconnaissance de formes. Elle fournit également des outils pour effectuer l'analyse en composantes principales et l'apprentissage de variétés (manifold learning) pour visualiser des jeux de données de haute dimension, ainsi qu'une suite d'algorithmes de clustering qui regroupent des données non étiquetées par partitionnement itératif. Le projet couvre un large éventail de capacités de modélisation prédictive, incluant des tâches de classification et de régression utilisant des arbres de décision, les k-plus proches voisins, les classificateurs de Bayes, les machines à vecteurs de support (SVM) et la régression ridge. Il inclut également des outils pour les flux de travail de classification d'images et l'analyse de données non étiquetées.

Orange3 is a visual data mining platform that provides an interactive canvas for building data analysis workflows without writing code. At its core, it offers a widget-based visual programming environment where users connect configurable components to perform data preprocessing, machine learning model training, statistical evaluation, and interactive visualization. The platform is built on NumPy-backed data tables with domain descriptors that define variable names, types, and roles, and includes a lazy SQL query proxy for working with database tables without loading all data into memory. The

Ce projet est une ressource pédagogique complète sur le machine learning, présentée sous forme d'une série de tutoriels dans des Jupyter Notebooks interactifs. Il propose des implémentations pratiques en Python pour l'ensemble du cycle de vie du machine learning, couvrant l'apprentissage supervisé et non supervisé, le deep learning et l'apprentissage par renforcement. La ressource se distingue par des guides d'implémentation détaillés pour des architectures complexes, notamment les transformers, les réseaux antagonistes génératifs (GAN) et les réseaux de neurones convolutifs. Elle propose également des cours spécialisés pour développer des agents d'apprentissage par renforcement utilisant le Q-learning et les Deep Q-Networks dans des environnements simulés. Le contenu couvre un large spectre de capacités en data science, incluant les pipelines d'ingénierie de données, l'encodage de caractéristiques et la réduction de dimensionnalité. Il fournit un matériel étendu sur l'évaluation des modèles via la validation croisée et des métriques de diagnostic, ainsi que des sujets avancés comme le traitement du langage naturel (NLP), l'analyse de sentiment et l'IA générative. L'ensemble du cursus est conçu pour une exécution interactive dans des Jupyter Notebooks, combinant code exécutable, texte riche et visualisations.

Side-Menu.Android est un composant d'interface utilisateur réutilisable pour les applications Android qui fournit un tiroir de navigation coulissant. Il est conçu pour aider les développeurs à organiser les sections de l'application et les options utilisateur dans un panneau structuré et masqué qui maintient une interface propre pour la zone de contenu principale. Le composant se distingue par sa présentation visuelle, qui suit les directives Material Design pour garantir une expérience utilisateur cohérente et intuitive. Il dispose d'une hiérarchie de menu pilotée par les données qui permet un regroupement logique des éléments de navigation, et il intègre des animations de révélation circulaire fluides pour fournir des transitions visuelles polies lorsque le menu est ouvert ou fermé. En encapsulant une logique de mise en page et d'interaction complexe dans une seule classe modulaire, la bibliothèque simplifie l'implémentation de la navigation sur plusieurs écrans. Elle prend en charge les transitions pilotées par les événements, permettant aux développeurs de découpler les interactions de menu des mises à jour de contenu pour maintenir une architecture d'application propre et réactive.

Kaolin est une bibliothèque de deep learning 3D PyTorch fournissant une suite complète d'outils pour le traitement de géométrie 3D, la simulation physique, la visualisation de données et le rendu basé sur le gradient pour la vision par ordinateur. La bibliothèque inclut un moteur de rendu 3D différentiable et une boîte à outils de traitement de géométrie pour convertir et transformer des représentations 3D telles que des maillages (meshes) et des nuages de points. Elle dispose également d'un moteur de simulation physique 3D pour calculer les interactions physiques et les collisions entre des objets et des scènes tridimensionnels. La boîte à outils fournit des utilitaires pour la visualisation de données 3D, incluant la création de vues interactives et d'animations de type plateau tournant. Les capacités supplémentaires couvrent la gestion des jeux de données 3D, le prétraitement des données et le rendu de représentations 3D.

This is an interactive notebook-based course that teaches machine learning from Python fundamentals through deep learning and natural language processing. It uses real datasets and multiple frameworks within a structured, hands-on curriculum that combines concise explanations with executable code cells, built-in datasets, and embedded exercise checkpoints. Learning progresses through data preparation and exploration, classical machine learning workflows, computer vision with convolutional neural networks, and natural language processing with deep learning, all delivered as a cohesive progressi