For the endeavor to work well, we need to compress the bloaty point cloud data down to an efficient format. The usual techniques -- JSON + gzip, protobuf, msgpack -- don't do very well with point clouds. Point cloud data are periodic, columnar, and fluffy. Each of these characteristics works to defeat the usual technique suspects. The periodic nature (point after point after point) doesn't give long runs of slightly different bytes to differentially compress. The columnar nature (X, Y, Z, Intensity, R, G, B, ReturnNumber, etc) and its binary packing exacerbates the failure of run-length encoding techniques. The fluffy nature comes from using wide integers to store narrow ranges of data. Format choices like storing the data in dimension-major storage (XXXXYYYZZZ) can give you the correlated runs of data that helps the popular algorithms, but the data organization cuts against you in other undesireable, detrimental ways.
LASzip is an application of arithmetic encoding by Martin Isenburg to compress the cyclical nature of point cloud data. LASzip is a fantastically powerful open source technology that I have been proud to help popularize to the world. It leverages the inherent correlation in point cloud data and exploits it to use accurate predictions to store smaller-in-bit-size residuals. It is differential encoding on steroids, buoyed by the application of an accurate model of the data that pushes fewer bits of residuals into the file storage. Currently applied to point clouds that come from LiDAR systems, many features of its models are applicable to other point cloud data -- things like SfM and Kinect-style sensors like Occipital/Tango.