# FSVVD: A Dataset of Full Scene Volumetric Video

arXiv: https://arxiv.org/abs/2303.03599 · HTML: https://mlsanity.com/paper/2303.03599/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- Builds on 5 of its 14 references that are in the map.
- First posted on arXiv 7 Mar 2023.

## Builds on (most cited first, 5 of 5)

- [StructureNet: Hierarchical Graph Networks for 3D Shape Generation](https://mlsanity.com/paper/1908.00575/) (2019)
- [HUMAN4D: A Human-Centric Multimodal Dataset for Motions and Immersive Media](https://mlsanity.com/paper/2110.07235/) (2021)
- [HUMBI: A Large Multiview Dataset of Human Body Expressions and Benchmark Challenge](https://mlsanity.com/paper/2110.00119/) (2021)
- [Deep Soft Procrustes for Markerless Volumetric Sensor Alignment](https://mlsanity.com/paper/2003.10176/) (2020)
- [Where Are You Looking?: A Large-Scale Dataset of Head and Gaze Behavior for 360-Degree Videos and a Pilot Study](https://mlsanity.com/paper/2208.04079/) (2022)

## Built on by (most cited first, 5 of 5)

- [TeleOR: Real-time Telemedicine System for Full-Scene Operating Room](https://mlsanity.com/paper/2407.19763/) (2024)
- [PackUV: Packed Gaussian UV Maps for 4D Volumetric Video](https://mlsanity.com/paper/2602.23040/) (2026)
- [Spatial Visibility and Temporal Dynamics: Revolutionizing Field of View Prediction in Adaptive Point Cloud Video Streaming](https://mlsanity.com/paper/2409.18236/) (2024)
- [EyeNavGS: A 6-DoF Navigation Dataset and Record-n-Replay Software for Real-World 3DGS Scenes in VR](https://mlsanity.com/paper/2506.02380/) (2025)
- [CPSL: Representing Volumetric Video via Content-Promoted Scene Layers](https://mlsanity.com/paper/2511.14927/) (2025)

## Abstract (arXiv)

Recent years have witnessed a rapid development of immersive multimedia which bridges the gap between the real world and virtual space. Volumetric videos, as an emerging representative 3D video paradigm that empowers extended reality, stand out to provide unprecedented immersive and interactive video watching experience. Despite the tremendous potential, the research towards 3D volumetric video is still in its infancy, relying on sufficient and complete datasets for further exploration. However, existing related volumetric video datasets mostly only include a single object, lacking details about the scene and the interaction between them. In this paper, we focus on the current most widely used data format, point cloud, and for the first time release a full-scene volumetric video dataset that includes multiple people and their daily activities interacting with the external environments. Comprehensive dataset description and analysis are conducted, with potential usage of this dataset. The dataset and additional tools can be accessed via the following website: https://cuhksz-inml.github.io/full_scene_volumetric_video_dataset/.