GH-NAF: Grid-Adaptive Hash-Level-Attended Neural Attenuation Fields for Discrepancy-Aware CBCT

Neural radiance fields (NeRF)-based methods with multi-resolution hash encoding enable efficient sparse-view CBCT reconstruction, but real-world projections violate ideal assumptions due to scatter/noise and related inconsistencies. Uniformly fusing hash-grid levels entangles heterogeneous frequency components, yielding spurious high-frequency textures in homogeneous tissues, blurred boundaries, and propagation of projection-induced bias. We propose GH-NAF (Grid-Adaptive Hash-Level-Attended Neural Attenuation Field), which decouples hash levels and adaptively weights them via uncertainty-guided, grid-adaptive hash-level attention. This stabilizes low-frequency modeling in homogeneous regions while selectively preserving high-frequency details near structural boundaries. Experiments on synthetic and real CBCT data show that GH-NAF improves intra-material contrast and reconstruction quality over state-of-the-art methods. The code is available at https://github.com/seongje-oh/GH-NAF