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GPU Volume Rendering with Hierarchical Compression Using VDB

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arxiv 2504.04564 v2 pith:EYUJVTPD submitted 2025-04-06 cs.GR cs.DC

Stefan Zellmann , Milan Jaros , Jefferson Amstutz , Ingo Wald This is my paper · ORCID
classification cs.GRcs.DC
keywords renderingvolumeapproachfastnanovdbopenvdbalmostcarlo
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We propose a compression-based approach to GPU rendering of large volumetric data using OpenVDB and NanoVDB. We use OpenVDB to create a lossy, fixed-rate compressed representation of the volume on the host, and use NanoVDB to perform fast, low-overhead, and on-the-fly decompression during rendering. We show that this approach is fast, works well even in a (incoherent) Monte Carlo path tracing context, can significantly reduce the memory requirements of volume rendering, and can be used as an almost drop-in replacement into existing 3D texture-based renderers.

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Cited by 2 Pith papers

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score. Full citation record

  1. ESVR: 3D Ellipsoid-based Sparse Volume Rendering via Structure-aware Primitive Learning and Per-primitive Ray Sampling

    cs.GR 2026-08 conditional novelty 7.0 of 10

    A method that fits differentiable ellipsoids to sparse volumetric intensity fields and renders them via per-primitive ray sampling, achieving real-time interaction for hundreds of gigabytes of data at high compression.

  2. Towards Fully Dynamic Omnitrees: Moment-Conserving Anisotropic Compression With Wavelets

    cs.DS 2026-07 conditional novelty 6.0 of 10

    Wavelet-guided coarsening and downsplit make omnitrees fully dynamic, delivering superior anisotropic compression versus OpenVDB on 3D shapes and continuous volumes while conserving mass by design.

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