37 个仓库
Visualizations that represent data in three-dimensional space to show volumetric or spatial relationships.
Distinct from Data Visualization: Distinct from general Data Visualization by specifically requiring a 3D coordinate system and GPU acceleration.
Explore 37 awesome GitHub repositories matching data & databases · Three-Dimensional Visualizations. Refine with filters or upvote what's useful.
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
Generates three-dimensional plots and globe visualizations to show volumetric or spatial relationships.
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
Enables the creation of three-dimensional plots and globes to represent complex spatial or volumetric data.
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
Enables the creation of immersive 3D data displays and interactive product showcases in the browser.
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
Renders processed point clouds and spatial data to allow visual inspection of three-dimensional environments.
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
Projects high-dimensional features into 2D space using t-SNE with interactive lasso selection for analysis.
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
Renders three dimensional models and datasets using a dedicated graphics engine.
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,
Projects a three-dimensional spherical model of the universe onto a two-dimensional view for the observer.
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
Implements visualizations that project high-dimensional layer outputs into lower-dimensional spaces using T-SNE to identify clusters.
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
Generates dimensionality projection plots to visually identify clusters and trends in complex datasets.
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
Renders high-dimensional embeddings into a 3D coordinate system for spatial analysis of data clusters.
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
Predicts facial feature locations in both two-dimensional image space and three-dimensional spatial coordinates.
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.
Predicts exact pixel locations on a 3D mesh by regressing values within a normalized UV map.
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
Maps non-Euclidean coordinates to Poincaré disk or Klein models to render curved space on flat screens.
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
Projects the Earth onto an icosahedron and tiles each face with hexagons to minimize distortion.
该项目是一个机器学习库,提供了一系列监督和无监督学习算法的实现。它作为一个深度学习框架、统计分类器集合,以及用于无监督学习和降维的工具套件。 该库支持构建神经网络,包括用于模式识别的多层感知器和卷积网络。它还提供用于执行主成分分析和流形学习以可视化高维数据集的工具,以及一套通过迭代分区对未标记数据进行分组的聚类算法。 该项目涵盖了广泛的预测建模功能,包括使用决策树、k-近邻、贝叶斯分类器、支持向量机和岭回归的分类与回归任务。它还包括用于图像分类工作流和未标记数据分析的工具。
Projects high-dimensional data into low-dimensional space via manifold learning for visual analysis.
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
Ships a unique force-directed projection method for visualizing class separations in labeled data.
This project is a comprehensive machine learning educational resource and tutorial series delivered as a collection of interactive Jupyter Notebooks. It provides practical Python implementations for the end-to-end machine learning lifecycle, covering supervised and unsupervised learning, deep learning, and reinforcement learning. The resource distinguishes itself by providing detailed implementation guides for complex architectures, including transformers, generative adversarial networks, and convolutional neural networks. It also features specialized courseware for developing reinforcement l
Implements techniques like t-SNE to map high-dimensional datasets into low-dimensional spaces for visual analysis.
Side-Menu.Android is a reusable UI component for Android applications that provides a slide-out navigation drawer. It is designed to help developers organize application sections and user options into a structured, hidden panel that maintains a clean interface for the primary content area. The component distinguishes itself through its visual presentation, which follows Material Design guidelines to ensure a consistent and intuitive user experience. It features a data-driven menu hierarchy that allows for logical grouping of navigation items, and it incorporates fluid circular reveal animatio
Generates three-dimensional models of cargo layouts to verify fit and spatial relationships.
Kaolin 是一个 PyTorch 3D 深度学习库,提供了一套全面的工具,用于 3D 几何处理、物理模拟、数据可视化和用于计算机视觉的梯度渲染。 该库包括一个可微分的 3D 渲染器和一个用于转换和变换 3D 表示(如网格和点云)的几何处理工具包。它还具有一个 3D 物理模拟引擎,用于计算三维物体和场景之间的物理交互和碰撞。 该工具包提供用于 3D 数据可视化的实用工具,包括创建交互式视图和转盘动画。其他功能涵盖 3D 数据集管理、数据预处理和 3D 表示渲染。
Renders interactive 3D visualizations and turntable animations for quick inspection of spatial data.
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
Projects complex datasets into two or three dimensions to visually identify patterns and clusters.