With the rapid advancement of large-scale scientific simulations, the massive volume of point cloud data generated has increasingly become a critical bottleneck for modern storage systems. Existing point cloud compression techniques used in data 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 issue arises in scientific data management pipelines for applications such as molecular dynamics simulations, which generate massive particle datasets forming dense distributions. Consequently, 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 performance 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.