Neural aberration fields: continuous implicit representation of spatially varying point spread functions for vision...

Convolution-based human vision simulation requires point spread functions (PSFs) that vary continuously across a high-dimensional parameter space encompassing field angle, pupil diameter, and eye-specific anatomy. Current state-of-the-art methods precompute PSFs on a discrete grid and rely on trilinear interpolation at runtime, incurring substantial memory overhead and resolution limitations. We propose Neural Aberration Fields (NAF), a framework that replaces discrete lookup tables with continuous neural implicit representations mapping optical coordinates directly to Zernike aberration coefficients. We systematically evaluate three neural encoding strategies-positional encoding, multiresolution hash encoding, and direct input-conditioned on patient-specific eye parameters, against the conventional trilinear scheme. Training data are generated from a physically grounded pipeline comprising a parametric four-surface eye model and Snell's-law ray tracing through 200,000 configurations spanning five ocular conditions. The positional-encoding variant (NAF-PE) achieves a Zernike coefficient MAE of 0.0032 mm and PSF PSNR of 53.3 dB while requiring only 139 KB of storage-an 84% memory reduction and 2.1× accuracy improvement over trilinear interpolation (MAE 0.0067 mm, 864 KB). On unseen cataract parameters, NAF-PE reduces MAE by 43% relative to the discrete baseline (0.0051 versus 0.0089 mm), demonstrating the generalization advantage of conditioned implicit representations. A conditioning ablation reveals that the accuracy gains stem primarily from patient-specific conditioning rather than representational capacity alone, identifying conditioning as a structural necessity for continuous PSF modeling. We further show that hash encoding achieves the highest single-configuration PSNR (56.1 dB) when the table capacity is matched to the data density, but degrades sharply when oversized. These results establish neural implicit fields as a viable, compact, and generalizable alternative to discrete PSF grids for real-time vision simulation individualized within parametric eye models.