Volcanite: Commodity-Hardware Segmentation Volume Visualization for Connectomics and Beyond
Abstract
Modern imaging produces terabyte-scale segmentation volumes, assigning each voxel an object label. These categorical, boundary-sensitive and label-rich data underpin connectomics and other imaging-driven fields, yet their scale often forces interpretation through slices, approximate meshes or distributed workflows that obscure spatial context and voxel-level defects. Here we show that such volumes can be explored directly on commodity hardware with Volcanite, an open-source framework for dense-segmentation rendering. Combining compression-aware data handling, a Vulkan GPU backend and segmentation-specific rendering, Volcanite enables low-latency exploration without meshing or distributed infrastructure, including for unpublished, sensitive or proprietary data. Across multi-domain datasets, it preserves voxel labels, adds shadows and global illumination, and renders up to half a trillion voxels at 100 frames per second. By replacing lengthy preprocessing with direct inspection, Volcanite decouples hypothesis generation from data preparation and turns teravoxel label fields into interactive evidence for discovery, validation and cross-domain spatial analysis
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BibTeX
@misc{piochowiak2026volcanite,
title = {{Volcanite: Commodity-Hardware Segmentation Volume Visualization for Connectomics and Beyond}},
author = {Max Piochowiak and Reiner Dolp and Julian Herold and Eric Behle and Carsten Dachsbacher and Alexander Schug},
howpublished = {arXiv},
year = {2026},
month = sep,
doi = {10.48550/arxiv.2609.36898},
eprint = {2609.36898},
archivePrefix = {arXiv},
url = {https://arxiv.org/abs/2609.36898},
}
RIS
TY - UNPB
TI - Volcanite: Commodity-Hardware Segmentation Volume Visualization for Connectomics and Beyond
AU - Max Piochowiak
AU - Reiner Dolp
AU - Julian Herold
AU - Eric Behle
AU - Carsten Dachsbacher
AU - Alexander Schug
PY - 2026
DA - 2026/09/29
PB - arXiv
DO - 10.48550/arxiv.2609.36898
UR - https://arxiv.org/abs/2609.36898
AB - Modern imaging produces terabyte-scale segmentation volumes, assigning each voxel an object label. These categorical, boundary-sensitive and label-rich data underpin connectomics and other imaging-driven fields, yet their scale often forces interpretation through slices, approximate meshes or distributed workflows that obscure spatial context and voxel-level defects. Here we show that such volumes can be explored directly on commodity hardware with Volcanite, an open-source framework for dense-segmentation rendering. Combining compression-aware data handling, a Vulkan GPU backend and segmentation-specific rendering, Volcanite enables low-latency exploration without meshing or distributed infrastructure, including for unpublished, sensitive or proprietary data. Across multi-domain datasets, it preserves voxel labels, adds shadows and global illumination, and renders up to half a trillion voxels at 100 frames per second. By replacing lengthy preprocessing with direct inspection, Volcanite decouples hypothesis generation from data preparation and turns teravoxel label fields into interactive evidence for discovery, validation and cross-domain spatial analysis
ER -