Recovering spatial-varying bi-directional reflectance distribution function (SVBRDF) from a few hand-held captured images has been a challenging task in computer graphics. Benefiting from the learned priors from data, single-image methods can obtain plausible SVBRDF estimation results. However, the extremely limited appearance information in a single image does not suffice for high-quality SVBRDF reconstruction. Although increasing the number of inputs can improve the reconstruction quality, it also affects the efficiency of real data capture and adds significant computational burdens. Therefore, the key challenge is to minimize the required number of inputs, while keeping high-quality results. To address this, we propose maximizing the effective information in each input through a novel co-located capture strategy that combines near-field and far-field point lighting. To further enhance effectiveness, we theoretically investigate the inherent relation between two images. The extracted relation is strongly correlated with the slope of specular reflectance, substantially enhancing the precision of roughness map estimation. Additionally, we designed the registration and denoising modules to meet the practical requirements of hand-held capture. Quantitative assessments and qualitative analysis have demonstrated that our method achieves superior SVBRDF estimations compared to previous approaches. All source codes will be publicly released.

中文翻译：

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NFPLight：通过近场和远场点照明的组合进行深度 SVBRDF 估计


从少量手持捕获的图像中恢复空间变化的双向反射分布函数 （SVBRDF） 一直是计算机图形学中一项具有挑战性的任务。受益于从数据中学习到的先验，单图像方法可以获得合理的 SVBRDF 估计结果。然而，单个图像中极其有限的外观信息不足以进行高质量的 SVBRDF 重建。虽然增加输入的数量可以提高重建质量，但它也会影响真实数据捕获的效率，并增加大量的计算负担。因此，关键挑战是最大限度地减少所需的输入数量，同时保持高质量的结果。为了解决这个问题，我们建议通过一种结合近场和远场点照明的新型共址捕获策略来最大化每个输入中的有效信息。为了进一步提高效率，我们从理论上研究了两个图像之间的内在关系。提取的关系与镜面反射率的斜率密切相关，大大提高了粗糙度图估计的精度。此外，我们还设计了配准和降噪模块，以满足手持采集的实际要求。定量评估和定性分析表明，与以前的方法相比，我们的方法实现了卓越的 SVBRDF 估计。所有源代码都将公开发布。