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This work has been supported by project "SARAOS" (PID2020-115132RB) funded by MCIN/AEI/10.13039/501100011033 of the Spanish Government and by project "XRECO" (HORIZON-IA-101070250) funded by the European Union.

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Analysis and Development of Deep Learning Depth Estimation Techniques for Volumetric Capture and Free Viewpoint Video

Publicado en: 520-523 - 2024-01-01 (), DOI: 10.1145/3625468.3652913

Autores: Usón, J; Cabrera, J

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Resumen

Volumetric capture is an important topic in eXtended Reality (XR) as it enables the integration of realistic three-dimensional content into virtual scenarios and immersive applications. Certain systems are even capable of delivering these volumetric captures live and in real-time, opening the door to interactive use cases such as immersive videoconferencing. One example of such systems is FVV Live, a Free Viewpoint Video (FVV) application capable of working in real-time with low delay Current breakthroughs in Artificial Intelligence (AI) in general and deep learning in particular report great success when applied to the computer vision tasks involved in volumetric capture, helping to overcome the quality and bandwidth restrictions that these systems often face. Despite their promising results, state-of-the-art approaches still come with the disadvantage of requiring large processing power and time. This project aims to advance the volumetric capture state-of-the-art applying the previously mentioned deep learning techniques, optimizing the models to work in real-time while still delivering high quality. The technology developed will be validated integrating it into immersive video communication systems such as FVV Live in order to overcome their main restrictions and to improve the quality delivered to the end user.

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