Abstract
In this article we present a survey of the different techniques of rendering of 3D computer generated images. We start with the principles and advances of the traditional methods of rasterization and ray tracing. Then, we discover the new techniques based on deep learning, which are now part of a new discipline of computer graphics called neural rendering, allowing the synthesis and rendering of 3D images, thanks to generative adversarial network and variational auto encoder models. Finally, we compare theses approaches according to different criteria.
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Halmaoui, H., Haqiq, A. (2022). Computer Graphics Rendering Survey: From Rasterization and Ray Tracing to Deep Learning. In: Abraham, A., et al. Innovations in Bio-Inspired Computing and Applications. IBICA 2021. Lecture Notes in Networks and Systems, vol 419. Springer, Cham. https://doi.org/10.1007/978-3-030-96299-9_51
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DOI: https://doi.org/10.1007/978-3-030-96299-9_51
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