Error-bounded Point Cloud Compression Using Truncated Octahedron Quantization

2026-08-01

AI Summary

Existing point cloud compression methods suffer from suboptimal rate-distortion performance due to a mismatch between their representation schemes and the characteristics of dense scientific data, resulting in high storage overhead and inefficient transmission and analysis. This work establishes, for the first time, a rate-distortion theoretic foundation tailored to dense point clouds and introduces an error-bounded compression framework. The proposed approach employs theoretically optimal truncated octahedron quantization, combined with space-filling curves and run-length encoding to achieve locality-aware, highly efficient compression. Evaluated on large-scale scientific datasets, the method achieves up to 3× higher compression ratios and accelerates compression and decompression by 2.2× and 1.2×, respectively, significantly outperforming state-of-the-art techniques.

Abstract

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.

Problem

• point cloud compression
• dense data
• rate-distortion trade-off
• scientific data management
• quantization

Innovation

• Truncated Octahedron Quantization
• Error-bounded Compression
• Dense Point Cloud
• Rate-distortion Optimization
• Locality-aware Encoding