Frame Filtering and Skipping for Point Cloud Data Video Transmission

Abstract

Sensors for collecting 3D spatial data from the real world are becoming more important. They are a prime research area topic and have applications in consumer markets, such as medical, entertainment, and robotics. However, a primary concern with collecting this data is the vast amount of information being generated, and thus, needing to be processed before being transmitted. To address the issue, we propose the use of filtering methods and frame skipping. To collect the 3D spatial data, called point clouds, we used the Microsoft Kinect sensor. In addition, we utilized the Point Cloud Library to process and filter the data being generated by the Kinect. Two different computers were used: a client which collects, filters, and transmits the point clouds; and a server that receives and visualizes the point clouds. The client is also checking for similarity in consecutive frames, skipping those that reach a similarity threshold. In order to compare the filtering methods and test the effectiveness of the frame skipping technique, quality of service (QoS) metrics such as frame rate and percentage of filter were introduced. These metrics indicate how well a certain combination of filtering method and frame skipping accomplishes the goal of transmitting point clouds from one location to another. We found that the pass through filter in conjunction with frame skipping provides the best relative QoS. However, results also show that there is still too much data for a satisfactory QoS. For a real-time system to provide reasonable end-toend quality, dynamic compression and progressive transmission need to be utilized.