pointcloudset: Efficient Analysis of Large Datasets of Point Clouds Recorded Over Time

Point clouds are a very common format for representing three dimensional data. Point clouds can be acquired by different sensor types and methods, such as lidar (light detection and ranging), radar (radio detection and ranging), RGB-D (red, green, blue, depth) cameras, photogrammetry, etc. In many cases multiple point clouds are recorded over time, e.g., automotive lidars record point clouds with very high acquisition frequencies (typically around 10-20Hz) resulting in millions of points per second. Analyzing such a large collection of point
clouds is a big challenge due to the huge amount of measurement data. The Python package pointcloudset provides a way to handle, analyse, and visualize large datasets consisting of multiple point clouds recorded over time. pointcloudset features lazy evaluation and parallel processing and is designed to enable development of new point cloud algorithms and
their application on big datasets.
pointcloudset builds on several well established Python libraries and packages for data processing and visualization, such as dask (Dask Development Team, 2016; Rocklin, 2015), pyntcloud (Pyntcloud Development Team, 2021), open3D (Zhou et al., 2018), plotly (Plotly Technologies Inc., 2015), and pandas (McKinney, 2010; The pandas development team, 2020).