LiDAR Software for ChromeOS

Browse free open source LiDAR software and projects for ChromeOS below. Use the toggles on the left to filter open source LiDAR software by OS, license, language, programming language, and project status.

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  • 1
    Intel RealSense

    Intel RealSense

    Intel® RealSense SDK

    Intel® RealSense™ SDK 2.0 is a cross-platform library for Intel® RealSense™ depth cameras. The SDK allows depth and color streaming and provides intrinsic and extrinsic calibration information. The library also offers synthetic streams (point cloud, depth aligned to color and vise-versa), and built-in support for recording and playback of streaming sessions. Intel has EOLed the LiDAR, Facial Authentication, and Tracking product lines. These products have been discontinued and will no longer be available for new orders. Intel WILL continue to sell and support stereo products including the following: D410, D415, D430, , D401 ,D450 modules and D415, D435, D435i, D435f, D405, D455, D457 depth cameras. We will also continue the work to support and develop our LibRealSense open source SDK.
    Downloads: 47 This Week
    Last Update:
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  • 2
    3Depict

    3Depict

    atom probe software : visualisation and data analysis

    This software is designed to help users visualize and analyze 3D point clouds with an associated real value, in a fast and flexible fashion. The primary use is in Atom Probe Tomography, which is an atomic imaging technique. However the program may also be useful in other areas, such as geospatial data, lidar, etc.
    Downloads: 3 This Week
    Last Update:
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  • 3

    MCC-LIDAR

    Multiscale Curvature Classification for LIDAR Data

    MCC-LIDAR is a C++ application for processing LiDAR data in forested environments. It classifies data points as ground or non-ground using the Multiscale Curvature Classification algorithm.
    Leader badge
    Downloads: 1 This Week
    Last Update:
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  • 4
    MATLAB Deep Learning Model Hub

    MATLAB Deep Learning Model Hub

    Discover pretrained models for deep learning in MATLAB

    Discover pre-trained models for deep learning in MATLAB. Pretrained image classification networks have already learned to extract powerful and informative features from natural images. Use them as a starting point to learn a new task using transfer learning. Inputs are RGB images, the output is the predicted label and score.
    Downloads: 0 This Week
    Last Update:
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