The compression of geometry data is an important aspect of
bandwidth-efficient data transfer for distributed 3d computer vision
applications. We propose a quantum-enabled lossy 3d point cloud compression
pipeline based on the constructive solid geometry (CSG) model representation.
Key parts of the pipeline are mapped to NP-complete problems for which an
efficient Ising formulation suitable for the execution on a Quantum Annealer
exists. We describe existing Ising formulations for the maximum clique search
problem and the smallest exact cover problem, both of which are important
building blocks of the proposed compression pipeline. Additionally, we discuss
the properties of the overall pipeline regarding result optimality and
described Ising formulations.Comment: 6 pages, 3 figure