Showing 6 open source projects for "gnu linux"

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  • 1
    CloudCompare

    CloudCompare

    CloudCompare main repository

    CloudCompare is a 3D point cloud (and triangular mesh) processing software. It was originally designed to perform comparison between two 3D points clouds (such as the ones obtained with a laser scanner) or between a point cloud and a triangular mesh. It relies on an octree structure that is highly optimized for this particular use-case. It was also meant to deal with huge point clouds (typically more than 10 million points, and up to 120 million with 2 GB of memory).
    Downloads: 49 This Week
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  • 2
    Manifold mesh processing

    Manifold mesh processing

    Mesh and point cloud processing tools

    A suite of tools for processing and analyzing meshes and point clouds. 1) Ensuring that meshes are manifold, ie, "nice" 2) Simple smoothing/noise reduction 3) Bare-bones point cloud to mesh and robust normals for point clouds 4) Mesh simplification 5) Curvature calculation on meshes 6) Local shape descriptors on meshes
    Downloads: 0 This Week
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  • 3
    ZPR is a program that produces orthophotos. The method used is to give colors to point-cloud (photo obtained by a digital camera, cloud obtained by a laser scanner) and to project the point-cloud to a requested plane. With this method, the projection pla
    Downloads: 0 This Week
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  • 4
    using simple cuboids and gjk algorithm, this general purpose algorithm gives a ready made code to work with swissranger 3D camera.
    Downloads: 0 This Week
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  • 5
    ### mesh reconstruction from point cloud # filters the input data eliminating noise # creates a quad shell - mesh with inside and thickness ### additional info: - GUI using Clan-Lib SDK
    Downloads: 0 This Week
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  • 6
    PhotoCloud is a tool to browse and a navigate for datasets composed of (a) a collection of registered images and (b) a 3D model over which images are aligned. The 3D model can be a point-cloud, triangle mesh. PhotoCloud is based on a client/server architecture and it is scalable: very large dataset (both in terms of (a) and (b) can be handled.
    Downloads: 0 This Week
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