论文标题
使用量子退火优化几何压缩
Optimizing Geometry Compression using Quantum Annealing
论文作者
论文摘要
几何数据的压缩是分布式3D计算机视觉应用的带宽有效数据传输的重要方面。我们根据建设性固体几何形状（CSG）模型表示，提出了一个具有量子的损失3D点云压缩管道。管道的关键部分被映射到NP完整问题，其中有效的ISING公式适合于量子退火器上的执行。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，这两者都是提议的压缩管道的重要组成部分。此外，我们讨论了有关结果最优性和描述的Ising公式的整体管道的属性。
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.
