Towards a Perceptual Evaluation Framework for Lighting Estimation
CoRR(2023)
摘要
Progress in lighting estimation is tracked by computing existing image
quality assessment (IQA) metrics on images from standard datasets. While this
may appear to be a reasonable approach, we demonstrate that doing so does not
correlate to human preference when the estimated lighting is used to relight a
virtual scene into a real photograph. To study this, we design a controlled
psychophysical experiment where human observers must choose their preference
amongst rendered scenes lit using a set of lighting estimation algorithms
selected from the recent literature, and use it to analyse how these algorithms
perform according to human perception. Then, we demonstrate that none of the
most popular IQA metrics from the literature, taken individually, correctly
represent human perception. Finally, we show that by learning a combination of
existing IQA metrics, we can more accurately represent human preference. This
provides a new perceptual framework to help evaluate future lighting estimation
algorithms.
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