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GeoTopoDiff framework reconstructs 3D porous microstructures from sparse CT slices

Researchers have developed GeoTopoDiff, a novel graph diffusion-based framework designed to reconstruct 3D porous microstructures from sparse CT slices. This approach shifts diffusion prior learning from a voxel space to a mixed graph state space, enabling simultaneous modeling of pore geometry and topology. Experiments on PTFE and Fontainebleau sandstone demonstrated significant reductions in morphology and transport errors, suggesting improved posterior uncertainty under sparse observations. AI

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IMPACT Introduces a new method for 3D reconstruction from sparse data, potentially improving simulations in materials science and engineering.

RANK_REASON This is a research paper detailing a new framework for 3D reconstruction.

Read on arXiv cs.CV →

COVERAGE [2]

  1. arXiv cs.CV TIER_1 · Yue Shi, Peng Wang, Mingzhe Yu, Yunlong Zhao, Li Liu, Gareth D Hatton, Yan Lyu, Liangxiu Han ·

    GeoTopoDiff: Learning Geometry--Topology Graph Priors through Boundary-Constrained Mixed Diffusion for Sparse-Slice 3D Porous Reconstruction

    arXiv:2605.03764v1 Announce Type: new Abstract: Diffusion-based voxel prior modelling is challenging for the reconstruction of large-scale 3D porous microstructures. Due to the demanding requirements for simultaneously modelling both the continuous pore morphology and the discret…

  2. arXiv cs.CV TIER_1 · Liangxiu Han ·

    GeoTopoDiff: Learning Geometry--Topology Graph Priors through Boundary-Constrained Mixed Diffusion for Sparse-Slice 3D Porous Reconstruction

    Diffusion-based voxel prior modelling is challenging for the reconstruction of large-scale 3D porous microstructures. Due to the demanding requirements for simultaneously modelling both the continuous pore morphology and the discrete pore-throat topology, the diffusion models req…