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.

中文翻译：

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使用量子退火优化几何压缩

几何数据的压缩是分布式 3D 计算机视觉应用的带宽高效数据传输的一个重要方面。我们提出了一种基于构造立体几何 (CSG) 模型表示的启用量子的有损 3d 点云压缩管道。管道的关键部分映射到 NP 完全问题，其中存在适合在量子退火器上执行的有效 Ising 公式。我们描述了最大集团搜索问题和最小精确覆盖问题的现有 Ising 公式，这两个问题都是所提出的压缩管道的重要构建块。此外，我们讨论了关于结果优化和描述的 Ising 公式的整体管道的属性。