“Automatic Vector Graphic Organization and Asset Extraction” by Dhanuka, Batra, Phogat and Dhingra

  • ©Praveen Kumar Dhanuka, Vineet Batra, Ankit Phogat, and Sumit Dhingra



Entry Number: 07


    Automatic Vector Graphic Organization and Asset Extraction



    We present a novel method for organizing Bézier bounded geometry based on affine similarity and visual saliency. For this computationally expensive (many-to-many) problem, we propose a highly parallel algorithm, leveraging programmable GPU pipeline, computing pairwise affine transformations to classify Bézier geometry into clusters, where paths in each cluster are affine transforms of each other. Using these clusters, we propose a method to organize paths into meaningful groups, even from complex, unstructured geometry, common in real-world vector art. We propose a function to quantify relative importance of these groups, using attributes such as complexity and frequency of occurrence, resulting in meaningful, reusable assets. Our method is both robust and performant, capable of processing thousands of paths within milliseconds.



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