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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.
X-Wim is built on a new phase-decoupled approach that breaks the strong interleaving between algorithmic phases, eliminates frequent global updates, enables concurrent search for multiple disjoint paths, and incorporates an adaptive search strategy, and is the fastest solution for this class of graph algorithms.
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