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.

翻译：可重光照物体采集是简化数字资产创建的关键挑战。完整重建物体通常需要在受控光照条件下使用专业设备拍摄数百至数千张照片。可微分渲染的最新进展提升了逆向渲染优化的质量与可及性。然而，在非受控光照与非结构化视角条件下，无法保证观测数据包含足够信息以重建被摄物体的外观属性。因此，我们提出从信号处理视角审视采集过程。在给定物体几何与光照环境的前提下，我们能在数秒内估算物体表面的材质属性。该方法通过频域分析，将材质属性恢复问题视为解卷积过程，从而实现快速误差估计。我们随后基于可用数据量化估计的不确定性，标定出需要先验知识或额外采样以提升采集质量的区域。通过与现有工作的比较及定量评估，本方法在达到相近质量的同时仅需极短时间，并为结果确定性提供了关键信息。