PC-LMT: The Point Cloud Log Merge Tree for the Helena Point Cloud Database

Point cloud data analysis and visualization workflows traditionally involve the sequential steps of information retrieval and preceding extensive data preparation. For example, visualizing large point clouds often takes days of processing to translate the data into a suitable representation before visual feedback is possible. While this works fine for static datasets and time-insensitive result consumption, it is unsuitable for dynamic contexts requiring real-time analysis, such as autonomous navigation. To address these shortcomings, we propose a point cloud data management approach based on a log-structured merge-tree that facilitates concurrent and continuous data ingestion and retrieval in real-time at scale. In this paper, we illustrate how to adapt this data structure to the peculiarities of point clouds and how various use cases and query modalities can be supported and optimized by specialized merge operations to repartition and refine the data structure and layout. This includes relying on grid-rounded coordinates and integrating importance as a means for effective and efficient storage, processing, and sampling from point clouds. Initial experiments and evaluation results display promising results and affirm the viability of this approach for Helena, a conceptual next-generation point cloud data management platform for interactive visualization and real-time analytics.