Dedenser's ability to reduce chemical point cloud membership to a target value is built around the Point Cloud Utils package, a Python interface to C++ functions. Dedenser utilizes the function `downsample_point_cloud_poisson_disk` to achieve blue noise distributions. These blue noise distributions are defined as being evenly distributed points with no bias toward a specific radius from some central point and therefore the points should be distributed independently and randomly (Figure ). Poisson disk sampling is utilized to downsample to a blue noise distribution. The Poisson disk downsampling function (f) as implemented in Point Cloud Utils is an iterative algorithm which will output a blue noise distributed point cloud (P*) when provided an initial point cloud (P) and a target number of points to keep (n). The Poisson disk represents a 3D disk in our case, though it generally may be N-dimensional, defined by a radius (r). Poisson disks are used to define a lower distance bound, ensuring that that no two points (p) are within a Poisson disk centered around any other point when (down)sampling. In our case, the distance (d) between points is the L2 norm in 3D Euclidean space (Euclidean distance), with the formalization of point cloud downsampling via Poisson disks displayed in eq. 1: