From Meshes to Gaussians: Evolving 3D Scene Rendering for Broadcast Applications

Metadata

Publisher: SMPTE
Doc Type: Journal Article
Content Type: Original Research
Abbreviated Title: SMPTE Motion Imaging Jour.
Volume: 135, No. 5, pp. 12–20
Abstract: This paper presents a technical overview of the evolving landscape of 3D scene representation and rendering, with a focus on photorealistic content generation in broadcast. While traditional polygon meshes offer highly efficient rendering, attaining convincing photorealism with them requires extensive manual modeling or complex capture setups. Breakthrough computer vision approaches have, however, recently enabled high photorealistic fidelity by jointly learning scene geometry, lighting, and appearance directly from images. A Neural Radiance Field (NeRF) encodes a scene implicitly as a continuous volumetric function, whereas 3D Gaussian Splatting (3DGS) provides an explicit representation through a set of Gaussian primitives. We introduce the fundamental principles of polygon meshes, NeRF, and 3DGS, then examine the applicability of NeRF and 3DGS in the broadcast domain, emphasizing both the progress realized by the research community and the challenges that remain for large-scale adoption in time-critical live production environments. Finally, we illustrate the potential of 3DGS through a preliminary case study in the demanding context of sports broadcasting.
Publication Date: 2026-09-01
DOI: 10.5594/JMI.2026/EAYD9580
ISSN: Print: 1545-0279 | Electronic: 2160-2492
Link: https://doi.org/10.5594/JMI.2026/EAYD9580
Author(s): Floriane Magera
Cedric Hons
Quentin Massoz
Thomas Hoyoux
Antoine Houet
Olivier Barnich
Keyword(s): 3D Rendering, 3D Scene Reconstruction, Meshes, Neural Radiance Fields, Gaussian Splatting
Copyright: © 2026 SMPTE

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Floriane Magera, Cedric Hons, Quentin Massoz, Thomas Hoyoux, Antoine Houet, and Olivier Barnich; From Meshes to Gaussians: Evolving 3D Scene Rendering for Broadcast Applications, MIJ 2026, Volume 135, Number 5 (pp. 12 to 20); SMPTE, 2026. Available at https://doi.org/10.5594/JMI.2026/EAYD9580

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Floriane Magera, Cedric Hons, Quentin Massoz, Thomas Hoyoux, Antoine Houet, and Olivier Barnich; From Meshes to Gaussians: Evolving 3D Scene Rendering for Broadcast Applications, MIJ 2026, Volume 135, Number 5 (pp. 12 to 20); SMPTE, 2026. Available at https://doi.org/10.5594/JMI.2026/EAYD9580

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<span class="citation">Floriane Magera, Cedric Hons, Quentin Massoz, Thomas Hoyoux, Antoine Houet, and Olivier Barnich; <cite>From Meshes to Gaussians: Evolving 3D Scene Rendering for Broadcast Applications</cite>, MIJ 2026, Volume 135, Number 5 (pp. 12 to 20); SMPTE, 2026. Available at <a href="https://doi.org/10.5594/JMI.2026/EAYD9580" target="_blank" rel="noopener">https://doi.org/10.5594/JMI.2026/EAYD9580</a></span>

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Floriane Magera, Cedric Hons, Quentin Massoz, Thomas Hoyoux, Antoine Houet, and Olivier Barnich; From Meshes to Gaussians: Evolving 3D Scene Rendering for Broadcast Applications, MIJ 2026, Volume 135, Number 5 (pp. 12 to 20); SMPTE, 2026
doi: 10.5594/JMI.2026/EAYD9580
url: https://doi.org/10.5594/JMI.2026/EAYD9580

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<li>
Floriane Magera, Cedric Hons, Quentin Massoz, Thomas Hoyoux, Antoine Houet, and Olivier Barnich; <cite id="bib-10-5594-jmi-2026-eayd9580">From Meshes to Gaussians: Evolving 3D Scene Rendering for Broadcast Applications</cite>, MIJ 2026, Volume 135, Number 5 (pp. 12 to 20); SMPTE, 2026
<span class="doi">10.5594/JMI.2026/EAYD9580</span>
</li>