Recently, Compressive Sensing (CS) has been successfully applied to multiple branches of science. However, most CS
methods require sequential capture of a large number of random data projections, which is not advantageous to LIDAR
systems, wherein reduction of 3D data sampling is desirable. In this paper, we introduce a new method called Resampling
Compressive Sensing (RCS) that can be applied to a single capture of a LIDAR point cloud to reconstruct a 3-
dimensional representation of the scene with a significant reduction in the required amount of data. Examples of 50 to
80% reduction in point count are shown for sample point cloud data. The proposed new CS method leads to a new data
collection paradigm that is general and different from traditional CS sensing such as the single-pixel camera architecture.