AI 中文总结
针对现有点云压缩技术不适用于密集科学数据的问题，提出基于截角八面体量化的XnYZip压缩器，可提升压缩比与处理速度，降低存储开销。
AI 中文摘要
随着大规模科学模拟的快速发展，生成的点云数据体量巨大，已日益成为科学存储系统与数据管理流程的关键瓶颈。现有集成到科学存储系统中的点云压缩技术专为稀疏几何设计，依赖的量化方案其最优性假设不适用于密集数据；将其应用于点云压缩层时，这种表示不匹配会导致根本上的次优率-失真权衡，无法通过参数调整或框架级适配解决，进而增加存储开销，限制模拟输出的高效传输与下游分析，在处理大规模密集粒子数据集的科学数据管理工作流中尤为突出。当前最先进的压缩方法无法充分利用此类数据固有的冗余性，我们通过构建密集数据的点云可压缩性理论，从表示层刻画基础率-失真行为来解决这一局限。基于该分析，我们提出XnYZip，这是一款基于可证最优截角八面体量化的误差受限有损压缩器，结合了使用空间填充曲线与游程编码的位置感知编码流程。对大规模科学数据集的实验表明，在相同失真条件下，与最先进的点云压缩器相比，该方法实现了一致的存储与吞吐量提升，压缩比最高提升3倍，压缩速度快2.2倍，解压速度快1.2倍。
英文摘要
With the rapid advancement of large-scale scientific simulations, the massive volume of point cloud data generated has increasingly become a critical bottleneck for scientific storage systems and data management pipelines. Existing point cloud compression techniques integrated into scientific storage systems are designed for sparse geometry and rely on quantization schemes whose optimality assumptions do not hold for dense data. When applied at the compression layer to point clouds, this representation mismatch leads to fundamentally sub-optimal rate-distortion trade-offs that cannot be addressed through parameter tuning or framework-level adaptations. This mismatch increases storage overhead and limits efficient movement and downstream analysis of simulation outputs. This issue arises in scientific data management workflows handling large-scale dense particle datasets. State-of-the-art compression methods fail to fully exploit the redundancies inherent in such data. We address this limitation by developing a theory of point cloud compressibility for dense data, characterizing fundamental rate-distortion behavior at the representation layer. Guided by this analysis, we introduce XnYZip, an error-bounded lossy compressor based on provably optimal Truncated Octahedron quantization, combined with a locality-aware encoding pipeline using space-filling curves and run-length encoding. Experiments on large-scale scientific datasets demonstrate consistent storage and throughput improvements, achieving up to 3x higher compression ratios, 2.2x faster compression, and 1.2x faster decompression compared to state-of-the-art point cloud compressors under same distortion.
Commentsaccepted by VLDB 2026
