A Pythonic Approach to Voxelizing Stereolithographic Models

    Research output: Working paper/PreprintWorking paper


    This paper investigates the efficacy of a voxelization algorithm implemented in Python’s programming interface. The algorithm combines a distance function with a vector based approach for the classification of voxels over stereolithographic domains. An approach for extracting surface models from the resultant volumetric array is also proposed while exploiting functionalities in three Python libraries.The robustness of the algorithm is tested with nine stereolithographic (STL) models with different levels of complexities and sizes. The algorithm was found to be suitable for STL models constituted by facets with lower aspect ratio and transition factor. However, several strategies are also proposed to promote the realisation of valid voxel models for other STL models containing irregular facets.
    Original languageEnglish
    Publisher Association for Computing Machinery
    Publication statusPublished - 2020


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