Point Cloud Data Management for Analytics in a Lakehouse

Abstract. Over time, the peculiarities of point clouds brought forth ample dedicated and specialized solutions for analyzing and managing point cloud data. However, providing analytical capabilities and visualization at scale remains challenging. We present a next-generation point cloud data management approach inspired by the Lakehouse pattern. It is exemplified by combining point clouds stored in raw files with a query engine, which instantly gives us an analysis-ready database management system with an SQL and DataFrame interface.We further demonstrate how to simplify and optimize this system through conversion to a columnar file format and a novel versatile repartitioning approach. Compared to existing solutions, the evaluation exhibits compelling performance, extraordinary flexibility, and exceptional simplicity.