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This paper introduces FeatureZ, a lossy compression framework for structured volumetric scalar fields that can preserve a wide class of geometric and topological features and demonstrates that FeatureZ preserves diverse features during compression with minimal overhead.
By enabling feature tracking directly on continuous representations, this method supports a new class of feature-driven visualization workflows centered on implicit models, and demonstrates the generality of this framework across a range of implicit representations, including analytic functions, MFAs, and INRs.
FaCTz is presented, the first GPU-based error-bounded lossy compressor that guarantees critical-point preservation and provides a block-wise mode optimized for throughput and a speculative per-point mode optimized for compression ratio.
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