This paper aims to quantify uncertainty for SVBRDF acquisition in multi-view captures. Under uncontrolled illumination and unstructured viewpoints, there is no guarantee that the observations contain enough information to reconstruct the appearance properties of a captured object. We study this ambiguity, or uncertainty, using entropy and accelerate the analysis by using the frequency domain, rather than the domain of incoming and outgoing viewing angles. The result is a method that computes a map of uncertainty over an entire object within a millisecond. We find that the frequency model allows us to recover SVBRDF parameters with competitive performance, that the accelerated entropy computation matches results with a physically-based path tracer, and that there is a positive correlation between error and uncertainty. We then show that the uncertainty map can be applied to improve SVBRDF acquisition using capture guidance, sharing information on the surface, and using a diffusion model to inpaint uncertain regions. Our code is available at https://github.com/rubenwiersma/svbrdf_uncertainty.

翻译：本文旨在量化多视角采集过程中空间变化双向反射分布函数（SVBRDF）的不确定性。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。我们通过熵值分析研究这种模糊性（即不确定性），并利用频域分析替代入射与出射视角域分析以加速计算过程。该方法可在毫秒级时间内计算物体全域的不确定性分布图。研究发现：频率模型能以竞争性性能恢复SVBRDF参数；加速熵值计算结果与基于物理的路径追踪器结果一致；误差与不确定性之间存在正相关关系。我们进一步证明，不确定性分布图可通过采集引导、表面信息共享以及扩散模型补全不确定区域等方式改进SVBRDF采集。代码已开源：https://github.com/rubenwiersma/svbrdf_uncertainty。