Confidence-driven planar element restoration for 3D volumetric data

We introduce a confidence-driven framework for addressing localized geometric distortions within 3D volumetric data through planar element restoration. Our method formulates a tripartite optimization objective: preserving high-confidence reference structures (HCRS) as spatial anchors, enforcing geometric plausibility via nearest-planar- point alignment of distorted coordinates, and minimizing global displacement energy in the corrected volume. A hybrid framework coupling robust RANSAC-based plane fitting with adaptive loss weighting enables nonlinear distortion correction under heterogeneous error models. Evaluation demonstrates efficacy against CT beam hardening artifacts and other small distortions affecting planarity through dual validation: (1) quantitative metrics on stochastic synthetic volumes, and (2) qualitative enhancement of PCB structure visibility.