$K$-NeAS: Scalable Multi-Material CT Reconstruction Using Neural SDFs
Computed Tomography (CT) carries significant ionizing radiation risks, driving the need for sparse-view reconstruction. Implicit scene representations (ISRs) address this by recovering continuous volumetric attenuation fields directly from sparse projections, and recent geometry-aware extensions jointly model surface geometry alongside attenuation to improve fidelity and enable clean tissue segmentation without manual thresholding. However, these methods remain limited by manually tuned attenuation bounds and rigid two-material constraints. This paper proposes $K$-NeAS, a unified and scalable architecture for automated, multi-material surface reconstruction. We replace independent materia...
| Authors | Daksh K. Shah, Emmanouil Nikolakakis, Razvan Marinescu |
|---|---|
| Primary category | cs.CV |
| Published | 15 July 2026 |
| Last updated at source | 2 September 2026 |
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