用知识蒸馏让神经反射成像更快更准，适合在普通设备上实时渲染高精度表面细节。
Fast and accurate neural reflectance transformation imaging through knowledge distillation
- 通过知识蒸馏训练小型网络，复现大型神经反射模型的性能
- 在保持与原模型相当质量的前提下，推理速度提升超过10倍
- 特别适合资源受限设备处理大尺寸高分辨率文物图像
反射变换成像（RTI）能通过交互式光照增强表面细节，仅需几十张固定相机拍摄、不同光照的照片即可实现。传统方法如多项式纹理图（PTM）和半球谐波（HSH）虽紧凑快速，但难以准确捕捉复杂反射场，尤其在高反光或阴影区域易产生伪影。神经反射成像（NeuralRTI）利用神经自动编码器学习像素级反射函数，显著提升质量且存储开销相近。然而其依赖参数量大的自定义解码器进行交互式光照渲染，计算成本高昂，无法在有限硬件上全分辨率运行。此前直接训练小网络的尝试均失败。为此，本文提出基于知识蒸馏的新型解决方案（DisK-NeuralRTI），成功将推理速度提升超过10倍，同时保持高质量输出，适用于大规模图像在普通设备上的实时应用。
原文摘要 · Abstract (English)
Reflectance Transformation Imaging (RTI) is very popular for its ability to visually analyze surfaces by enhancing surface details through interactive relighting, starting from only a few tens of photographs taken with a fixed camera and variable illumination. Traditional methods like Polynomial Texture Maps (PTM) and Hemispherical Harmonics (HSH) are compact and fast, but struggle to accurately capture complex reflectance fields using few per-pixel coefficients and fixed bases, leading to artifacts, especially in highly reflective or shadowed areas. The NeuralRTI approach, which exploits a neural autoencoder to learn a compact function that better approximates the local reflectance as a function of light directions, has been shown to produce superior quality at comparable storage cost. However, as it performs interactive relighting with custom decoder networks with many parameters, the rendering step is computationally expensive and not feasible at full resolution for large images on limited hardware. Earlier attempts to reduce costs by directly training smaller networks have failed to produce valid results. For this reason, we propose to reduce its computational cost through a novel solution based on Knowledge Distillation (DisK-NeuralRTI). ...
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