Kammerl, Julius (Technische Universität München), Blodow, Nico (Technische Universität München), Rusu, Radu Bogdan (Willow Garage, Inc), Gedikli, Suat (Willow Garage Inc.), Beetz, Michael (Technische Universität München), Steinbach, Eckehard (Munich University of Technology)

Real-Time Compression of Point Cloud Streams

Scheduled for presentation during the Regular Session "3D Surface Models, Point Cloud Processing" (TuB08), Tuesday, May 15, 2012, 11:30−11:45, Meeting Room 8 (Wacipi)

2012 IEEE International Conference on Robotics and Automation, May 14-18, 2012, RiverCentre, Saint Paul, Minnesota, USA

This information is tentative and subject to change. Compiled on September 23, 2021

Abstract

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.