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COLMAP is a 3D scene reconstruction suite and C++ geometry library that implements a full structure-from-motion pipeline. It functions as a GPU-accelerated photogrammetry tool and multi-view stereo framework designed to produce dense 3D geometry and watertight meshes from collections of 2D images. The project distinguishes itself through hardware-accelerated feature extraction and a modular camera modeling system that supports perspective, fisheye, and equirectangular lens types. It employs vocabulary tree image retrieval to efficiently identify similar images in large datasets and provides P
Kornia is a differentiable computer vision library and cross-framework tensor vision toolset. It implements vision operations as differentiable tensors to enable integration into deep learning pipelines and supports the transpilation of operations across PyTorch, TensorFlow, JAX, and NumPy. The project provides specialized toolsets for geometric vision and stereo depth, including algorithms for 3D scene reconstruction, camera calibration, and pose estimation. It further distinguishes itself as a differentiable image augmentation framework, applying random geometric and color transformations w
PaddleClas is a toolkit for image classification and recognition built on PaddlePaddle. It provides a suite of tools for training deep learning models and a framework for implementing visual search and retrieval systems. The project includes a computer vision model optimization suite and tools for cross-platform deployment. It enables the export of trained models to servers, mobile devices, and edge hardware to achieve high-performance inference across different programming languages. The toolkit covers model compression and optimization through pruning, quantization, and knowledge distillat
EasyMocap is a markerless 3D human motion capture system that recovers body, hand, and face poses from single or multi-view video without physical markers or suits. It uses parametric body models like SMPL, SMPL-X, and MANO, and leverages mirror reflections to resolve depth ambiguity in single-view pose estimation, improving accuracy by computing mirror surface normals from vanishing points. The system distinguishes itself through mirror-assisted depth disambiguation, enabling accurate 3D pose reconstruction from a single RGB image or video that includes a mirror reflection. It also supports
This project is a 3D visual localization framework designed to determine a camera's exact position and orientation by matching 2D image features against a 3D reference model. It includes a structure-from-motion pipeline to reconstruct 3D scene geometry from unordered image sets, creating the necessary spatial maps for localization.
The main features of cvg/hierarchical-localization are: Visual Localization, 3D Pose Estimation, Visual Localization Frameworks, Pose Refinement Pipelines, Visual Feature Extractors, Feature Extraction, Deep Feature Extractors, Pose Estimation.
Open-source alternatives to cvg/hierarchical-localization include: colmap/colmap — COLMAP is a 3D scene reconstruction suite and C++ geometry library that implements a full structure-from-motion… kornia/kornia — Kornia is a differentiable computer vision library and cross-framework tensor vision toolset. It implements vision… zju3dv/easymocap — EasyMocap is a markerless 3D human motion capture system that recovers body, hand, and face poses from single or… paddlepaddle/paddleclas — PaddleClas is a toolkit for image classification and recognition built on PaddlePaddle. It provides a suite of tools… openmvg/openmvg — openMVG is a computer vision geometry library and toolkit for multiple view geometry. It serves as a framework for… fchollet/deep-learning-models — This project is a collection of deep learning tools for image classification and audio tagging, providing a repository…