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Intrinsic image decomposition and inverse rendering are long-standingproblems in computer vision. To evaluate albedo recovery, most algorithmsreport their quantitative performance with a mean Weighted Human DisagreementRate (WHDR) metric on the IIW dataset. However, WHDR focuses only on relativealbedo values and often fails to capture overall quality of the albedo. Inorder to comprehensively evaluate albedo, we collect a new dataset, MeasuredAlbedo in the Wild (MAW), and propose three new metrics that complement WHDR:intensity, chromaticity and texture metrics. We show that existing algorithmsoften improve WHDR metric but perform poorly on other metrics. We then finetunedifferent algorithms on our MAW dataset to significantly improve the quality ofthe reconstructed albedo both quantitatively and qualitatively. Since theproposed intensity, chromaticity, and texture metrics and the WHDR are allcomplementary we further introduce a relative performance measure that capturesaverage performance. By analysing existing algorithms we show that there issignificant room for improvement. Our dataset and evaluation metrics willenable researchers to develop algorithms that improve albedo reconstruction.Code and Data available at: https://measuredalbedo.github.io/

Measured Albedo in the Wild: Filling the Gap in Intrinsics Evaluation