Showing 4 open source projects for "surface defect detection"

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  • 1
    Mesh R-CNN

    Mesh R-CNN

    code for Mesh R-CNN, ICCV 2019

    ...The model learns to detect, segment, and reconstruct detailed 3D mesh representations of objects in natural images, bridging the gap between 2D perception and 3D understanding. Unlike voxel-based or point-based approaches, Mesh R-CNN uses a differentiable mesh representation, allowing it to efficiently refine surface geometry while maintaining high spatial detail. The system combines 2D detection from Mask R-CNN with 3D reasoning modules that output full mesh reconstructions aligned with the input image. It has been evaluated on datasets such as Pix3D, where it demonstrates state-of-the-art performance in reconstructing real-world object geometry.
    Downloads: 2 This Week
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  • 2

    NEtCAT NanoSurface Analyzer

    NanoSurface Analyzertool is designed for the STM image analysis

    NanoSurface Analyzer tool is designed for the analysis and measurement of surface data at the nanoscale. This application is developed to support researchers working with surface data by providing tools for reading, processing, and analyzing data.
    Downloads: 0 This Week
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  • 3
    Surface Defect Detection Dataset Papers

    Surface Defect Detection Dataset Papers

    Constantly summarizing open source dataset and critical papers

    At present, surface defect equipment based on machine vision has widely replaced artificial visual inspection in various industrial fields, including 3C, automobiles, home appliances, machinery manufacturing, semiconductors and electronics, chemical, pharmaceutical, aerospace, light industry and other industries. Traditional surface defect detection methods based on machine vision often use conventional image processing algorithms or artificially designed features plus classifiers. ...
    Downloads: 0 This Week
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  • 4
    Objectron

    Objectron

    A dataset of short, object-centric video clips

    The Objectron dataset is a collection of short, object-centric video clips, which are accompanied by AR session metadata that includes camera poses, sparse point-clouds and characterization of the planar surfaces in the surrounding environment. In each video, the camera moves around the object, capturing it from different angles. The data also contain manually annotated 3D bounding boxes for each object, which describe the object’s position, orientation, and dimensions. The dataset consists...
    Downloads: 0 This Week
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