Real-time Indexing and Visualization of LiDAR Point Clouds with Arbitrary Attributes using the M3NO Data Structure

In previous work, we have presented an approach to index 3D LiDAR point clouds in real time, i.e. while they are being recorded. We have further introduced a novel data structure called M3NO, which allows arbitrary attributes to be indexed directly during data acquisition. Based on this, we now present an integrated approach that supports not only real-time indexing but also visualization with attribute filtering. We specifically focus on large datasets from airborne and land-based mobile mapping systems. Compared to traditional indexing approaches running offline, the M3NO is created incrementally. This enables dynamic queries based on spatial extent and value ranges of arbitrary attributes. The points in the data structure are assigned to levels of detail (LOD), which can be used to create interactive visualizations. This is in contrast to other approaches, which focus on either spatial or attribute indexing, only support a limited set of attributes, or do not support real-time visualization. Using several publicly available large data sets, we evaluate the approach, assess quality and query performance, and compare it with existing state-of-the-art indexing solutions. The results show that our data structure is able to index 5.24 million points per second. This is more than most commercially available laser scanners can record and proves that low-latency visualization during the capturing process is possible.