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From Capture to Display: A Survey on Volumetric Video

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arxiv 2309.05658 v2 pith:4SX52O3O submitted 2023-09-11 cs.MM cs.NIeess.IV

Yili Jin , Kaiyuan Hu , Junhua Liu , Fangxin Wang , Xue Liu This is my paper · ORCID
classification cs.MMcs.NIeess.IV
keywords videovolumetricservicessurveychallengesdisplaypotentialresearch
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Volumetric video, which offers immersive viewing experiences, is gaining increasing prominence. With its six degrees of freedom, it provides viewers with greater immersion and interactivity compared to traditional videos. Despite their potential, volumetric video services pose significant challenges. This survey conducts a comprehensive review of the existing literature on volumetric video. We firstly provide a general framework of volumetric video services, followed by a discussion on prerequisites for volumetric video, encompassing representations, open datasets, and quality assessment metrics. Then we delve into the current methodologies for each stage of the volumetric video service pipeline, detailing capturing, compression, transmission, rendering, and display techniques. Lastly, we explore various applications enabled by this pioneering technology and we present an array of research challenges and opportunities in the domain of volumetric video services. This survey aspires to provide a holistic understanding of this burgeoning field and shed light on potential future research trajectories, aiming to bring the vision of volumetric video to fruition.

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Cited by 2 Pith papers

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score. Full citation record

  1. ABE-VVS: Attribute-Based Encrypted Volumetric Video Streaming

    cs.CR 2026-01 conditional novelty 4.0 of 10

    Encrypting only X coordinates of point clouds with attribute-based encryption can obfuscate volumetric video while reducing encryption/decryption time and server/cache CPU load in streaming.

  2. Adaptive 3D Gaussian Splatting Video Streaming: Visual Saliency-Aware Tiling and Meta-Learning-Based Bitrate Adaptation

    cs.CV 2025-07 conditional novelty 4.0 of 10

    A saliency-aware tiling and meta-learning bitrate control system for streaming 3D Gaussian splatting video, claimed to outperform existing methods.

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