Showing 4 open source projects for "collections"

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  • 1
    COLMAP

    COLMAP

    Structure-from-Motion and Multi-View Stereo

    COLMAP is a general-purpose Structure-from-Motion (SfM) and Multi-View Stereo (MVS) pipeline with a graphical and command-line interface. It offers a wide range of features for the reconstruction of ordered and unordered image collections. The software is licensed under the new BSD license.
    Downloads: 36 This Week
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  • 2
    AliceVision

    AliceVision

    3D Computer Vision Framework

    AliceVision is an open-source photogrammetric computer vision framework designed to reconstruct detailed 3D scenes and camera motion from collections of images or videos. It provides a complete pipeline for structure-from-motion (SfM), multi-view stereo (MVS), and mesh generation, allowing users to convert 2D imagery into accurate 3D models. The framework is built with a strong emphasis on research-grade algorithms while maintaining the robustness required for production environments, making it suitable for industries such as visual effects, cultural heritage preservation, and robotics. ...
    Downloads: 0 This Week
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  • 3
    Bolt ML

    Bolt ML

    10x faster matrix and vector operations

    Bolt is an open-source research project focused on accelerating machine learning and data mining workloads through efficient vector compression and approximate computation techniques. The core idea behind Bolt is to compress large collections of dense numeric vectors and perform mathematical operations directly on the compressed representations instead of decompressing them first. This approach significantly reduces both memory usage and computational overhead when working with high-dimensional data commonly used in machine learning systems. Bolt is particularly useful in applications such as similarity search, approximate nearest neighbor queries, and large-scale matrix computations where millions of vectors must be processed efficiently. ...
    Downloads: 0 This Week
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  • 4
    GibbsLDA++: A C/C++ Implementation of Latent Dirichlet Allocation (LDA) using Gibbs Sampling for parameter estimation and inference. GibbsLDA++ is fast and is designed to analyze hidden/latent topic structures of large-scale (text) data collections.
    Downloads: 0 This Week
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