Stars
The repository provides code for running inference and finetuning with the Meta Segment Anything Model 3 (SAM 3), links for downloading the trained model checkpoints, and example notebooks that sho…
MapAnything: Universal Feed-Forward Metric 3D Reconstruction
An open source SDK for logging, storing, querying, and visualizing multimodal and multi-rate data
Reference PyTorch implementation and models for DINOv3
ViPE: Video Pose Engine for Geometric 3D Perception
Code of π^3: Permutation-Equivariant Visual Geometry Learning
AnyCalib: On-Manifold Learning for Model-Agnostic Single-View Camera Calibration (ICCV 2025)
Community list of open-source GNSS software and resources 📡
Release repo for our SLAM Handbook
[CVPR 2025 Best Paper Award] VGGT: Visual Geometry Grounded Transformer
[CVPR 2025] MASt3R-SLAM: Real-Time Dense SLAM with 3D Reconstruction Priors
An open source platform for visual-inertial navigation research.
[CVPR 2025 Highlight] Official implementation of the solvers and estimators proposed in the paper "Relative Pose Estimation through Affine Corrections of Monocular Depth Priors"
GeoCalib: Learning Single-image Calibration with Geometric Optimization (ECCV 2024)
GTSAM is a library of C++ classes that implement smoothing and mapping (SAM) in robotics and vision, using factor graphs and Bayes networks as the underlying computing paradigm rather than sparse m…
[SIGGRAPH Asia'24 & TOG] Gaussian Opacity Fields: Efficient Adaptive Surface Reconstruction in Unbounded Scenes
code for the TOG 2023 paper "Digital 3D Smocking Design"
Vanishing Point Estimation in Uncalibrated Images with Prior Gravity Direction (ICCV 2023)
Original reference implementation of "3D Gaussian Splatting for Real-Time Radiance Field Rendering"
[CVPR 2023] Learning a Depth Covariance Function
LightGlue: Local Feature Matching at Light Speed (ICCV 2023)
Official implementation of "Decentralization and Acceleration Enables Large-Scale Bundle Adjustment"
Joint Deep Matcher for Points and Lines 🖼️💥🖼️ (ICCV 2023)
The repository provides code for running inference with the SegmentAnything Model (SAM), links for downloading the trained model checkpoints, and example notebooks that show how to use the model.