Awesome GitHub RepositoriesGeometric Deep Learning Frameworks

DGL is a Python library for building and training graph neural networks. It functions as a graph message passing framework and a geometric deep learning tool, enabling the development of models that analyze graph-structured data. The library is designed for large-scale graph processing, utilizing distributed training and neighbor sampling to handle datasets with billions of edges. It provides specialized support for heterogeneous graph modeling, allowing for the representation of complex real-world entities with multiple node and edge types. Its capabilities cover a wide range of graph tasks

PyTorch3D is a 3D geometric deep learning library and mesh processing toolkit designed for learning from point clouds and complex 3D surface geometries. It provides a collection of reusable components and data structures for deep learning with 3D data, including a framework for training and evaluating neural radiance fields to enable photorealistic view synthesis. The project features a differentiable 3D renderer that converts meshes and point clouds into 2D images while allowing gradients to flow back into the geometry and textures. This enables 3D shape optimization, where mesh geometry, te

PCDet is a LiDAR 3D object detection toolbox and point cloud processing library built on the PyTorch deep learning framework. It provides a system for identifying and locating three-dimensional objects within point cloud data. The project utilizes a data-model separation pattern to decouple dataset loading logic from the core detection pipeline. It features a multi-sensor fusion pipeline that combines data from multiple sensors into a shared spatial view and a distributed GPU training system to scale workloads across multiple graphics processors. The toolkit covers several capability areas,

graph_nets is a graph-structured deep learning framework and library for building message-passing neural networks. It provides tools for designing architectures that operate on nodes and edges to process and reason about data structured as graphs using TensorFlow. The framework implements a message-passing paradigm for iterative information exchange between nodes. This approach enables the development of models that can reason about complex graph-structured inputs for tasks such as path-finding and sorting, or serve as a predictor for the future states and trajectories of physical systems.

SpatialLM एक स्थानिक मॉडलिंग फ्रेमवर्क है जो मोनोकुलर वीडियो और सेंसर डेटा को स्ट्रक्चर्ड इंडोर सिमेंटिक मैप्स में बदलने के लिए लार्ज लैंग्वेज मॉडल्स का उपयोग करता है। यह इंडोर लेआउट एस्टिमेशन और पॉइंट क्लाउड सिमेंटिक पार्सर के रूप में कार्य करता है, जो रॉ जियोमेट्रिक डेटा को आर्किटेक्चरल तत्वों और ऑब्जेक्ट श्रेणियों के प्रतिनिधित्व में बदलता है। यह प्रोजेक्ट मल्टी-मोडल सेंसर इनपुट को भाषाई टोकन के साथ अलाइन करता है, जिससे एक लैंग्वेज मॉडल रूम टोपोलॉजी का अनुमान लगाने के लिए रीजनिंग इंजन के रूप में कार्य कर सकता है। यह 3D पॉइंट क्लाउड्स और 2D इमेज सीक्वेंस को डिस्क्रीट टोकन और स्ट्रक्चर्ड स्थानिक एन्कोडिंग में बदलने के लिए मैकेनिज्म का उपयोग करता है, जिन्हें बाद में आर्किटेक्चरल लेआउट में डिकोड किया जाता है। यह फ्रेमवर्क बाउंडिंग बॉक्स और सिमेंटिक लेबल के माध्यम से फर्नीचर की पहचान करने के लिए 3D सीन एनालिसिस और ऑब्जेक्ट डिटेक्शन को कवर करता है। यह रोबोट के लिए पर्यावरणीय समझ के टूल्स भी प्रदान करता है, जो स्वायत्त नेविगेशन के लिए सिमेंटिक मैप बनाने हेतु सेंसर डेटा को प्रोसेस करता है।

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

PointNet++ is a deep learning framework designed for processing and classifying 3D point cloud data. It utilizes a hierarchical feature learning architecture to extract geometric patterns from sampled 3D point sets. The framework implements a variety of 3D analysis tools, including a point cloud classifier for categorizing objects based on spatial coordinates and surface normals, a semantic scene segmenter for labeling surfaces in large-scale environments, and a tool for 3D object part segmentation. The system covers a broad range of capabilities including geometric feature extraction, 3D da