Skeleton-Based Data Compression for Multi-Camera Tele-Immersion System
Source: University of California
Image-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. This paper introduces 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 the paper addresses the issues regarding the data capturing and transfer to a remote site for the tele-immersive collaboration. The paper compares the results of the existing compression methods and the proposed skeleton-based compression technique. The paper examines robustness and efficiency of the algorithm through experimental results with the multi-camera tele-immersion system.