Quality-Aware Compression of Point Clouds with Google Draco

Abstract

Situational awareness is getting traction in the field of autonomous inland vessels. Large amounts of data needs to be shared in order to set up this awareness. This ranges from relatively small positional updates, to consistent streams of sensory data. Point clouds, captured by LiDAR sensors, are heavily used by inland vessels as they give a detailed sense of range. This sharing is not a major complexity when vessels are in close proximity with each other; dedicated networks could handle this consistent stream of data. However, when vessels are farther away from each other, long range networks are needed, at the cost of high bandwidth capabilities. Therefore, the sensor message size should be reduced while retaining a reasonable quality. In this paper, we investigate the trade-off between lossless and lossy point cloud compression with Google Draco and its resulting quality. The results show that a considerable size reduction can be applied while the point cloud maintains acceptable quality.