Name
Abstract | ||
|---|---|---|
Vision-based full-body 3D reconstruction for tele-immersive applications generates large amount of data points, which have
to be sent through the network in real time. In this paper, we introduce a skeleton-based compression method using motion
estimation where kinematic parameters of the human body are extracted from the point cloud data in each frame. First we address
the issues regarding the data capturing and transfer to a remote site for the tele-immersive collaboration. We compare the
results of the existing compression methods and the proposed skeleton-based compression technique. We examine the robustness
and efficiency of the algorithm through experimental results with our multi-camera tele-immersion system. The proposed skeleton-based
method provides high and flexible compression ratios from 50:1 to 5000:1 with reasonable reconstruction quality (peak signal-to-noise
ratio from 28 to 31 dB) while preserving real-time (10+ fps) processing. |
Year | DOI | Venue |
|---|---|---|
2010 | 10.1007/s00371-009-0367-8 | The Visual Computer |
Keywords | DocType | Volume |
3d tele-immersion · multi-camera tele-immersion system · point data compression · model-based compression,point cloud,compression ratio,real time,human body,data compression,peak signal to noise ratio,motion estimation,3d reconstruction,data capture | Journal | 26 |
Issue | ISSN | Citations |
1 | 1432-2315 | 12 |
PageRank | References | Authors |
1.06 | 35 | 3 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Jyh-ming Lien | 1 | 651 | 50.28 |
| Gregorij Kurillo | 2 | 494 | 34.71 |
| Ruzena Bajcsy | 3 | 3621 | 869.56 |