Awesome GitHub RepositoriesMulti-Modal Data Processors

This project is an artificial intelligence-powered frontend generator that translates visual design inputs into functional source code. It functions as a workflow engine that interprets graphical user interfaces, mapping layout structures and styling rules to structured markup and programming language syntax. The tool distinguishes itself by supporting both static design mockups and dynamic video recordings. It processes temporal and spatial information from screen captures to reconstruct interaction flows and state transitions, enabling the creation of functional software prototypes from vis

The Model Context Protocol SDK is a framework for building clients and servers that connect AI models to external data, tools, and resources using a standardized communication protocol. It provides the foundational libraries and interfaces necessary to establish reliable, transport-agnostic connections between AI agents and external systems, enabling seamless information retrieval and task automation. The SDK distinguishes itself through a robust capability negotiation handshake that ensures compatibility between connected parties before exchanging messages. It supports a pluggable transport

MMDetection3D is an open-source toolbox for 3D perception, providing a unified framework for detecting and segmenting objects in three-dimensional environments. It supports a range of core tasks including monocular 3D object detection from single camera images, LiDAR-based 3D object detection from raw point clouds, and multi-modal fusion that combines camera images with LiDAR data. The toolbox also covers point cloud semantic segmentation, assigning class labels to every point in a scan for scene understanding. The project distinguishes itself through a config-driven pipeline that orchestrate

SpatialLM este un framework de modelare spațială care utilizează modele de limbaj mari (LLM) pentru a transforma datele video monoculare și cele de la senzori în hărți semantice structurate de interior. Acesta funcționează ca un sistem pentru estimarea layout-ului interior și ca un parser semantic pentru nori de puncte, convertind datele geometrice brute în reprezentări ale elementelor arhitecturale și ale categoriilor de obiecte. Proiectul aliniază input-urile senzorilor multimodali cu token-uri lingvistice, permițând unui model de limbaj să servească drept motor de raționament pentru deducerea topologiei încăperilor. Utilizează mecanisme pentru a converti norii de puncte 3D și secvențele de imagini 2D în token-uri discrete și codificări spațiale structurate, care sunt apoi decodificate în layout-uri arhitecturale. Framework-ul acoperă analiza scenelor 3D și detectarea obiectelor pentru a identifica mobilierul prin bounding box-uri și etichete semantice. De asemenea, oferă instrumente pentru înțelegerea mediului de către roboți, procesând datele senzorilor pentru a crea hărți semantice necesare navigației autonome.

This project is a technical reference guide and sensor-based robotics manual focused on the theoretical foundations and practical implementation of Simultaneous Localization and Mapping. It serves as a knowledge base for spatial AI, covering the integration of deep learning and semantic rendering to create intelligent systems for open world environments. The resource provides guidance on integrating multi-modal sensor data from cameras, LiDAR, radar, and inertial sensors for localization and mapping. It also establishes a bibliographic standard for robotics research by providing systems for m

FAST-LIVO2 is a LiDAR-inertial odometry framework and factor-graph SLAM implementation designed for real-time robot localization and 3D mapping. It functions as a multi-sensor fusion pipeline and state estimator that integrates LiDAR, inertial, and camera inputs to track a robot's position and orientation. The system employs a tightly-coupled sensor fusion approach to maintain stable navigation, particularly in degraded environments. It utilizes a voxel-based 3D mapping tool to organize point clouds into volumetric grids, which optimizes memory usage and search speed during spatial reconstruc