View-Dependent Streaming of Dynamic Point Cloud over Hybrid Networks.

Characterized by efficient and exquisite representation of the objects or scenarios in the real world, 3D point cloud has been widely applied in large amount of emerging applications such as virtual reality/augmented reality, automatic drive, gaming technologies or robotics. Each point of the data contains 3D geometry information and corresponding photometry information like color, intensity, normal or texture, leading to massive data capacity and severely influence the transmission quality with limited network resources. However, more than a half of the points in each point cloud frame are invisible as being occluded by others from the main viewpoint. To deal with the above issues, we propose a view-dependent streaming for dynamic point cloud based on the novel hybrid networks. We project the point cloud frame into six 2D frames and generate videos with different bitrates in consideration of various user interests. Therefore, differential transmission can be achieved such that the personalized contents like the current consumed viewpoint are transmitted via interactive broadband channel, while the less-attention contents can be pushed through general digital broadcasting channel. Therefore, benefit from existing hybrid transmission systems, reliable services with efficient utilization of limited transmission resources are achieved. Experimental results have shown considerable bandwidth saving based on the proposed scheme, maintaining satisfying reconstruction performance.