Showing 1-20 of 30 projects
A highly-starred open-source SLAM (Simultaneous Localization and Mapping) library for monocular, stereo, and RGB-D cameras.
A foundation model for monocular depth estimation that leverages large-scale unlabeled data.
A Python library for fast, high-quality monocular view synthesis, useful for computer vision and 3D applications.
A highly capable foundation model for monocular depth estimation, a key component in computer vision.
A robust and versatile monocular visual-inertial state estimator for robotics and computer vision applications.
Code for robust monocular depth estimation, a key computer vision task for AI-powered apps.
A Python library for fast, accurate monocular depth estimation using machine learning.
This repository provides a PyTorch implementation for monocular depth estimation from a single image.
Python/C++ Visual SLAM pipeline for 3D reconstruction
A comprehensive collection of papers and datasets for 3D point cloud processing, useful for developers working on autonomous driving and computer vision.
A GitHub repository providing a metric depth estimation model from a single image using deep learning.
LSD-SLAM is a real-time monocular SLAM (Simultaneous Localization and Mapping) algorithm written in C++.
A PyTorch-based library for consistent depth estimation in super-long videos using transformers.
Detailed comments for ORB-SLAM2, a popular C++ library for real-time monocular, stereo and RGB-D SLAM.
Estimates dense, flicker-free, geometrically consistent depth from monocular video for computer vision applications.
A versatile and accurate open-source monocular SLAM library for computer vision and robotics applications.
A Python library for 3D multi-person mesh recovery from monocular images.
Obsolete search and discovery UI for Helm Chart repositories, focused on Kubernetes developers.
Visual-inertial state estimation library for mobile phones, enabling accurate pose tracking.
A free and open-source tool that converts images to 3D parallax effect videos using AI depth estimation.
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