Content
In this paper we released:
- 45 dynamic sequences of humans in social settings
-
- Single and multi user scenarios
- Recorded with commodity hardware
- Raw data (including audio) + generated point clouds
- Software suite for
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- Real-time capturing
- Encoding
- Post-processing
- Rendering of dynamic sequences
- Single and multi user scenarios
- Recorded with commodity hardware
- Raw data (including audio) + generated point clouds
- Real-time capturing
- Encoding
- Post-processing
- Rendering of dynamic sequences
Abstract
Real-time, immersive telecommunication systems are quickly becoming a reality, thanks to the advances in acquisition, transmission, and rendering technologies. Point clouds in particular serve as a promising representation in these type of systems, offering photorealistic rendering capabilities with low complexity. Further development of transmission, coding, and quality evaluation algorithms, though, is currently hindered by the lack of publicly available datasets that represent realistic use cases of remote communication between people in real-time.
In this paper, we release a dynamic point cloud dataset that depicts humans interacting in social XR settings. Using commodity hardware, we capture a total of 45 unique sequences, according to several use cases for social XR. As part of our release, we provide annotated raw material, resulting point cloud sequences, and an auxiliary software toolbox to acquire, process, encode, and visualize data, suitable for real-time applications.
Figure 2. Illustration of RGB and depth raw data captured by 7 Kinect Azure DK devices that comprise our capturing system, and corresponding point cloud frames that are generated offline
Reference
If you used the dataset or found this work useful, please cite:
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CWIPC-SXR: Point Cloud dynamic human dataset for Social XR.
12th ACM Multimedia Systems Conference (MMSys’21),
(): pp. ,
September 28-October 1, 2021.