Towards a Perceptual Evaluation Framework for Lighting Estimation

Figure 1 . We pit image comparison metrics, used to quantify the performance of lighting estimation algorithms, against human perception. When asked which render looks most plausible, our controlled psychophysical study reveals that humans preference contradicts image metrics in the vast majority of cases. This paper questions the current practice of employing image quality metrics for evaluating lighting estimation algorithms when used for the task of virtual object insertion: can we do better by considering human perception?