Relightable object acquisition is a key challenge in simplifying digital
asset creation. Complete reconstruction of an object typically requires
capturing hundreds to thousands of photographs under controlled illumination,
with specialized equipment. The recent progress in differentiable rendering
improved the quality and accessibility of inverse rendering optimization.
Nevertheless, under uncontrolled illumination and unstructured viewpoints,
there is no guarantee that the observations contain enough information to
reconstruct the appearance properties of the captured object.
We thus propose to consider the acquisition process from a signal-processing
perspective. Given an object's geometry and a lighting environment, we estimate
the properties of the materials on the object's surface in seconds. We do so by
leveraging frequency domain analysis, considering the recovery of material
properties as a deconvolution, enabling fast error estimation. We then quantify
the uncertainty of the estimation, based on the available data, highlighting
the areas for which priors or additional samples would be required for improved
acquisition quality. We compare our approach to previous work and
quantitatively evaluate our results, showing similar quality as previous work
in a fraction of the time, and providing key information about the certainty of
the results.

通过信号处理的角度，我们提出从几秒钟内估计物体表面材质的属性，借助频域分析将材质属性恢复作为一种去卷积，以快速估计误差，并根据可用数据量化估计的不确定性，突出需要先验知识或额外样本以提高采集质量的区域，与之前的工作相比较，定量评估我们的结果，表明在更短的时间内提供类似质量的结果，并提供关于结果确定性的关键信息。