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 ratedistortion 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 3× higher compression ratios, 2.2× faster compression, and 1.2× faster decompression compared to state-of-the-art point cloud compressors under same distortion.
You-Yuan Liu, Longtao Zhang, Ruoyu Li et al.· Proceedings of the VLDB Endo...· 0 citations
Lossy compression is essential for managing massive scientific data, but per-element error bounds do not translate into bounds on downstream quantities of interest (QoIs) such as regional averages, neural network predictions, or multi-field derived quantities. We present TOPIQ, a statistical error-propagation framework that predicts QoI-level bias and uncertainty from compact compression metadata (less than 0.1% of original data). TOPIQ decomposes QoIs into primitive operators with closed-form propagation rules accounting for spatial error correlation and data-error coupling; new QoIs are supported by composition at runtime with no per-QoI derivation or retraining. Across 552 evaluations spanning 4 datasets, 3 compressors, 4 QoI families, and 8 error bounds, 93.1% of configurations achieve well-calibrated predictions. Pre-computed metadata enables post-hoc uncertainty quantification for arbitrary query regions at 56x-402x speedup over direct computation. A case study demonstrates integration into an AI-driven analysis pipeline with end-to-end confidence intervals for dynamically composed queries.
You-Yuan Liu, Bo Jiang, Taolue Yang et al.· 0 citations
ParticleGS is a visualization-aware framework based on 3D Gaussian Splatting (3DGS) that learns a compact representation directly optimized for rendered image quality, combining a multi-stage, multi-orbit training pipeline and a lightweight network that adapts a single trained model to user-specified visualization parameters at inference time.
Bo Jiang, You-Yuan Liu, Taolue Yang et al.· arXiv.org· 1 citation
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