NeuVolEx extracts robust spatial features from INR training via a structural encoder and multi-task scheme to enable accurate ROI classification with limited supervision and unsupervised viewpoint clustering in volume exploration.
In: IEEE Transactions on Visualization and Computer Graphics
2 Pith papers cite this work. Polarity classification is still indexing.
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Pith papers citing it
years
2026 2verdicts
UNVERDICTED 2representative citing papers
A hybrid Gaussian-hash-grid representation with latent features and hard opacity falloffs reconstructs scenes more accurately than prior Gaussian splatting methods while using an order of magnitude fewer primitives.
citing papers explorer
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NeuVolEx: Implicit Neural Features for Volume Exploration
NeuVolEx extracts robust spatial features from INR training via a structural encoder and multi-task scheme to enable accurate ROI classification with limited supervision and unsupervised viewpoint clustering in volume exploration.
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Hybrid Latents: Geometry-Appearance-Aware Surfel Splatting
A hybrid Gaussian-hash-grid representation with latent features and hard opacity falloffs reconstructs scenes more accurately than prior Gaussian splatting methods while using an order of magnitude fewer primitives.