Graph Neural Network Library for PyTorch
- pytorch
- geometric-deep-learning
- graph-neural-networks
- deep-learning
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Graph Neural Network Library for PyTorch
[ECCV 2026 Best Paper Award Candidate] LingBot-Map: Geometric Context Transformer for Streaming 3D Reconstruction
[CVPR 2025 Best Paper Award] VGGT: Visual Geometry Grounded Transformer
COLMAP - Structure-from-Motion and Multi-View Stereo
🐍 Geometric Computer Vision Library for Spatial AI
StellarGraph - Machine Learning on Graphs
An open source library and framework for deep learning on satellite and aerial imagery.
Implementation of DiffDock: Diffusion Steps, Twists, and Turns for Molecular Docking
An autoML framework & toolkit for machine learning on graphs.
QuadriFlow: A Scalable and Robust Method for Quadrangulation
Robust Tetrahedral Meshing in the Wild.
Official Hi3D v1.0: high-resolution 3D geometry refinement built on Sparc3D. Inference code, model weights, and documentation.
GEARS is a geometric deep learning model that predicts outcomes of novel multi-gene perturbations
TriWild: Robust Triangulation with Curve Constraints
The official implementation of 'S4VY: Segment Anything in Feed-Forward 4D Visual Geometry'
High-performance Piecewise Geometric Model (PGM) learned index implementation in pure Rust with strict epsilon error guarantees
A tool for quantitative risk analysis of Android applications based on machine learning techniques
PyTorch Geometric Signed Directed is a signed/directed graph neural network extension library for PyTorch Geometric. The paper is accepted by LoG 2023.
Legend of Elya — N64 game with a real 6.36M-parameter ternary transformer on the VR4300 MIPS III CPU. Zelda-style dungeon, AI NPCs, byte-level inference at 1.23 tok/s scalar / 2.19 tok/s on the RSP overlay (measured under ares, never on silicon). Built with libdragon.
This repo is the official implementation of "F2G-Pose: Geometry-Aware Foundation Feature Lifting for Direct RGB-D Category-Level Object Pose Estimation".
A TensorFlow implementation of the Mnemonic Descent Method.
We would like to maintain a list of resources which aim to solve molecular docking and other closely related tasks.
A graph reliability toolbox based on PyTorch and PyTorch Geometric (PyG).
Multi-stage Riemannian flow matching for physically valid molecular docking, with GNINA scoring, PoseBusters filtering, CLI inference, and benchmarks.