We present Spatons, a family of compositional, spatially localized neural operators for computationally efficient XR (extended reality) predistortion and distortion compensation. XR displays (AR, VR, and MR) suffer from spatial (e.g., geometric) and tonal (e.g., colour) distortions. Frames can be pre-distorted so that the projected image looks as intended. Distortions vary with manufacturing tolerances and projection systems, motivating programmable compensation. Untethered, battery-operated XR systems also require low energy and latency. A Spaton is a hybrid patch-to-pixel block combining tiny Multi-Layer Perceptrons (MLPs) with fixed-function interpolation. Using Spatons, we build programmable, computationally efficient pipelines for varied distortion scenarios. On the full spatiotonal Scenario 5, Sequential-5' matches CompenNet++ in SSIM (0.944 vs. 0.944) and closely matches its PSNR (27.5 vs. 27.7 dB), with substantially lower complexity. We demonstrate a flexible Spaton solution with 6 times fewer FLOPs and 106 times fewer parameters than CompenNet++, and 4-10 times fewer memory transfers than UNet++. We characterize software implementations and analytical hardware estimates, including optimizations across target XR platforms. The distortions considered are drawn mostly from see-through, near-eye displays, where the spatial light path governs local intensity and spectral response. This early systems-level exploration uses closed-form differentiable distortions and one real projector-camera capture set for training and evaluation, rather than through-the-optics measurements. Testing on a wider range of real XR devices remains future work. Chromatic dispersion (color fringes) remains the main artifact where Spatons trail monolithic baselines.

翻译：暂无翻译