Real-time compression of point cloud streams

We present a novel lossy compression approach for point cloud streams which exploits spatial and temporal redundancy within the point data. Our proposed compression framework can handle general point cloud streams of arbitrary and varying size, point order and point density. Furthermore, it allows for controlling coding complexity and coding precision. To compress the point clouds, we perform a spatial decomposition based on octree data structures. Additionally, we present a technique for comparing the octree data structures of consecutive point clouds. By encoding their structural differences, we can successively extend the point clouds at the decoder. In this way, we are able to detect and remove temporal redundancy from the point cloud data stream. Our experimental results show a strong compression performance of a ratio of 14 at 1 mm coordinate precision and up to 40 at a coordinate precision of 9 mm.

I. Introduction

Recently, 3D data acquisition systems have become increasingly wide-spread and as technology is advancing, so is scan resolution and accuracy. Sensors such as the Microsoft Kinect, stereo camera systems or high-speed, high-resolution laser scanners can produce large amounts of point data at high frame rates. This sensor data can be efficiently represented by point clouds from which a robot can infer information about the shape and geometry of their environment, detect objects as well as to orient itself [1]. Additional applications are apparent in the field of 3D video and multimedia. However, due to the large size of the point clouds, the processing and transmission of point cloud information occupies a significant amount of resources. Particularly, the real-time transmission of point clouds in the context of remote data processing or in teleoperation scenarios is challenging (see Fig. 1). For bandwidth-limited scenarios such as online streaming or storage, it therefore becomes necessary to develop efficient compression solutions.