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Recovering shadows is an important step for many vision algorithms. Currentapproaches that work with time-lapse sequences are limited to simplethresholding heuristics. We show these approaches only work with very carefultuning of parameters, and do not work well for long-term time-lapse sequencestaken over the span of many months. We introduce a parameter-free expectationmaximization approach which simultaneously estimates shadows, albedo, surfacenormals, and skylight. This approach is more accurate than previous methods,works over both very short and very long sequences, and is robust to theeffects of nonlinear camera response. Finally, we demonstrate that the shadowmasks derived through this algorithm substantially improve the performance ofsun-based photometric stereo compared to earlier shadow mask estimation.

Shadow Estimation Method for "The Episolar Constraint: Monocular Shape from Shadow Correspondence"