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Code for "MatchAnything: Universal Cross-Modality Image Matching with Large-Scale Pre-Training", Arxiv 2025.
Official implementation of Lotus: Diffusion-based Visual Foundation Model for High-quality Dense Prediction
[NeurIPS'24] NeuRodin: A Two-stage Framework for High-Fidelity Neural Surface Reconstruction
Code for "Detector-Free Structure from Motion", CVPR 2024
Code release for CVPR'24 submission 'OmniGlue'
Implementation of XFeat (CVPR 2024). Do you need robust and fast local feature extraction? You are in the right place!
🌟A curated list of DUSt3R-related papers and resources, tracking recent advancements using this geometric foundation model.
[3DV 2025] Code for "FlowMap: High-Quality Camera Poses, Intrinsics, and Depth via Gradient Descent" by Cameron Smith*, David Charatan*, Ayush Tewari, and Vincent Sitzmann
This repository will house builds of Unity's synthetic home generator.
[IROS 2024] Representing 3D sparse map points and lines for camera relocalization
[CVPR 2021] Deep Two-View Structure-from-Motion Revisited
Open source Structure-from-Motion pipeline
[NeurIPS'22] MonoSDF: Exploring Monocular Geometric Cues for Neural Implicit Surface Reconstruction
Using traditional image processing techniques to construct 3D point cloud of objects. Incremental Structure from Motion (SfM) is used, a popular SfM algorithm for 3D reconstruction for reconstructi…
[CVPR 2024 - Oral, Best Paper Award Candidate] Marigold: Repurposing Diffusion-Based Image Generators for Monocular Depth Estimation
Code for robust monocular depth estimation described in "Ranftl et. al., Towards Robust Monocular Depth Estimation: Mixing Datasets for Zero-shot Cross-dataset Transfer, TPAMI 2022"
A Unified Framework for Surface Reconstruction
Use WebVR today, without requiring a special browser build.
[CVPR 2024] Official PyTorch implementation of SuGaR: Surface-Aligned Gaussian Splatting for Efficient 3D Mesh Reconstruction and High-Quality Mesh Rendering
magcut and Image Stitching with HTML5 Canvas and JavaScript
Demonstration of MobileSAM in the browser enabled through ONNX runtime web
This is the official code for MobileSAM project that makes SAM lightweight for mobile applications and beyond!
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.