GeoTopoDiff transfers diffusion priors to a mixed graph state space with topology-aware constraints from sparse slices, cutting morphology errors by 19.8% and transport errors by 36.5% on PTFE and sandstone samples.
International Conference on Learning Representations , year =
2 Pith papers cite this work. Polarity classification is still indexing.
2
Pith papers citing it
citation-role summary
background 1
citation-polarity summary
fields
cs.CV 2years
2026 2verdicts
UNVERDICTED 2roles
background 1polarities
background 1representative citing papers
Reparameterizations create invariances in diffusion inverse-problem solvers, enabling hyperparameter reuse and accelerated inference via the OptDiff optimization framework.
citing papers explorer
-
GeoTopoDiff: Learning Geometry--Topology Graph Priors through Boundary-Constrained Mixed Diffusion for Sparse-Slice 3D Porous Reconstruction
GeoTopoDiff transfers diffusion priors to a mixed graph state space with topology-aware constraints from sparse slices, cutting morphology errors by 19.8% and transport errors by 36.5% on PTFE and sandstone samples.
-
Principled Design of Diffusion-based Optimizers for Inverse Problems
Reparameterizations create invariances in diffusion inverse-problem solvers, enabling hyperparameter reuse and accelerated inference via the OptDiff optimization framework.