Abstract: Systems, methods, and other embodiments are disclosed for identifying features within point cloud data. In one embodiment, point cloud data is read which represents multiple points of at least one point cloud in a multi-dimensional space. Each point in the point cloud data is defined by an attribute value quantifying an attribute of the point and a set of coordinates specifying a location of the point in the multi-dimensional space. The set of coordinates for each point is transformed into a space-filling distance value representing a distance along a space-filling curve. The points are sorted according to the space-filling distance values to generate a sorted order of the points. The points are traversed in the sorted order and output data points are derived, while traversing the points, based on a specified feature criterion. The output data points identify a feature within the at least one point cloud.

Abstract: Systems, methods, and other embodiments are disclosed for identifying features within point cloud data. In one embodiment, point cloud data is read which represents multiple points of at least one point cloud in a multi-dimensional space. Each point in the point cloud data is defined by an attribute value quantifying an attribute of the point and a set of coordinates specifying a location of the point in the multi-dimensional space. The set of coordinates for each point is transformed into a space-filling distance value representing a distance along a space-filling curve. The points are sorted according to the space-filling distance values to generate a sorted order of the points. The points are traversed in the sorted order and output data points are derived, while traversing the points, based on a specified feature criterion. The output data points identify a feature within the at least one point cloud.