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An Empirical Study of the Effect of Point Cloud Compression on the Performance of Segmentation

Object and scene representation by sets of point coordinates possibly complemented with color or other information, i.e. point clouds, is being used in several application areas like entertainment, terrain representation, architectural layout documentation and archiving and more recently, autonomous vehicle guidance systems. Due to the large size of point clouds, some of these applications require the use of compression to tackle the challenges of storage and real-time transmission of the point clouds. Since lossy compression of the point clouds information introduces geometric distortions, and given that in some cases reconstructed/decompressed point clouds will be further processed to segment its component objects, it is important to understand and characterize the effect of the type and degree of compression on the performance of the segmentation tasks. In this article we describe a set of experiments designed to empirically evaluate the effect of three different point cloud compression methods, employed at different compression rates, on the performance of three common point cloud segmentation algorithms.