This paper discusses the benefits of VVglTF.

Abstract

Volumetric Video (VV) lets viewers interact with realistic virtual 3D scenes in real-time. It enhances the quality and realism of video broadcasting and conferencing, delivering a vivid and immersive 3D experience. Holographic communication, concert and VR experiences benefit from it. VV data is often recorded as point clouds or meshes with shape and texture data. This leads to a substantial volume of data that requires efficient compression and streaming. Nevertheless, there is a lack of a universally accepted standards for VV file formats, resulting in many organizations implementing their own individualized approaches. Hence, we suggest VVglTF, a specific extension that supports the glTF file format and ensures VV compatibility. The open source 3D content file format glTF is optimized for the Internet. We utilize glTF’s rendering workflow to play VV of any duration or file size efficiently. Custom extensions in glTF expand the functionality of the glTF model format and offer customization of VV playback. We also developed a simple and efficient VVglTF streaming system based on HTTP Live Streaming technology for video texturing. It is designed to efficiently play VV content across different network conditions by adjusting the frame rate and number of frames per glTF file. In our experiments we validate the efficiency of our approach.

Introduction

Volumetric video (VV) is a type of video, typically created by simultaneously capturing and reconstructing 3D scenes from various perspectives, often including humans or objects, with the aim of producing an authentic and engaging experience for its viewers. VV technology brings real people into virtual spaces and facilitates 3D interaction inside the Metaverse. VV pipelines can include four major modules as shown in Figure 1, from Volumetric Capture, Volumetric Processing, Volumetric Encoding to Decoding and Rendering. VV is produced by utilizing cameras, sensors and software, which collaborate to acquire and analyse data from several viewpoints.

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