real-time compression for dynamic 3d environments
The goal of tele-immersion has long been to enable people at remote locations to share a sense of presence. A tele-immersion system acquires the 3D representation of a collaborator's environment remotely and sends it over the network where it is rendered in the user's environment. Acquisition, reconstruction, transmission, and rendering all have to be done in real-time to create a sense of presence. With added commodity hardware resources, parallelism can increase the acquisition volume and reconstruction data quality while maintaining real-time performance. However this is not as easy for rendering since all of the data need to be combined into a single display.In this paper we present an algorithm to compress data from such 3D environments in real-time to solve this imbalance. We expect the compression algorithm to scale comparably to the acquisition and reconstruction, reduce network transmission bandwidth, and reduce the rendering requirement for real-time performance. We have tested the algorithm using a synthetic office data set and have achieved a 5 to 1 compression for 22 depth streams.
real-time compression tele-immersion k-means algorithm k-means initialization virtual reality