UVG-CWI-DQPC: Dual-Quality Point Cloud Dataset for Volumetric Video Applications

DOI:10.4121/38af920a-b4d1-4356-aea1-cd46acbc4cbe.v1
The DOI displayed above is for this specific version of this dataset, which is currently the latest. Newer versions may be published in the future. For a link that will always point to the latest version, please use
DOI: 10.4121/38af920a-b4d1-4356-aea1-cd46acbc4cbe

Datacite citation style

Gautier, Guillaume; Zhou, Xuemei; Nguyen, Thong; Jansen, Jack; Fréneau, Louis et. al. (2025): UVG-CWI-DQPC: Dual-Quality Point Cloud Dataset for Volumetric Video Applications. Version 1. 4TU.ResearchData. dataset. https://doi.org/10.4121/38af920a-b4d1-4356-aea1-cd46acbc4cbe.v1
Other citation styles (APA, Harvard, MLA, Vancouver, Chicago, IEEE) available at Datacite

Dataset

Volumetric video is a key enabler of immersive extended reality experiences and is often represented using point clouds for their structural simplicity. However, capturing volumetric content through multi-view acquisition and depth sensing poses many challenges, such as occlusions and depth mismatches. To foster research in this field, we introduce a unique dual-quality point cloud dataset, named UVG-CWI-DQPC, which is designed to support the development of point cloud enhancement, compression, and quality assessment. Our dataset includes 12 dynamic sequences captured simultaneously by: 1) a high-end capture system producing high-fidelity point clouds with extensive processing; and 2) a consumer-grade capture system relying on affordable RGB-D cameras, lightweight processing, and open-source tools. For each sequence, our dataset provides ground-truth point clouds from the high-end capture system and raw RGB-D footage from the consumer-grade capture system, along with calibration data and tools for point cloud generation. This dual-quality setup enables direct comparison and benchmarking of algorithms for densification, occlusion removal, registration, and quality enhancement. Our dataset is publicly available under a permissive license to support reproducible research and standardization work in Moving Picture Experts Group and 3rd Generation Partnership Project.

History

  • 2025-10-03 first online, published, posted

Publisher

4TU.ResearchData

Format

3D point cloud (.ply)

Organizations

TU Delft, Faculty of Electrical Engineering, Mathematics and Computer Science, Department of Intelligent Systems, Computer Graphics and Visualisation
Tampere University, Faculty of Information Technology and Communication Sciences, Computing
Sciences

DATA - not available

Becasue the dataset is is openly accessible at Tampere University and quite large