Using Fractional Super-Resolution to Improve Lossy Compression of Point Cloud Geometry

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Tomás M. Borges
Renan Utida Barbosa Ferreira
Diogo C. Garcia
Ricardo L. de Queiroz

Abstract

We present a method for post-processing point clouds’ geometric information by applying a previously proposed fractional super-resolution technique to clouds compressed and decoded with MPEG’s G-PCC codec. In some sense, this is a
continuation of that previous work, which requires only a downscaled point cloud and a scaling factor, both of which are provided by the G-PCC codec. For non-solid point clouds, an a priori down-scaling is required for improved efficiency. The method is compared to the GPCC itself, as well as machine-learning-based techniques. Results show a great improvement in quality over GPCC and comparable performance to the latter techniques, with the advantage of not needing any kind of previous training.

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How to Cite
Borges, T. M., Ferreira, R. U. B., Garcia, D. C., & de Queiroz, R. L. (2023). Using Fractional Super-Resolution to Improve Lossy Compression of Point Cloud Geometry. Journal of Communication and Information Systems, 38(1), 169–173. https://doi.org/10.14209/jcis.2023.19
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Letters

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