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We present a method for estimating intrinsic images from a xed-viewp oint image sequence captured under changing illumination di- rections. Previous work on this problem reduces the inuence of shadows on reectance images, but does not address shading eects which can sig- nican tly degrade reectance image estimation under the typically biased sampling of illumination directions. In this paper, we describe how biased illumination sampling leads to biased estimates of reectance image deriva- tives. To avoid the eects of illumination bias, we propose a solution that explicitly models spatial and temporal constraints over the image sequence. With this constraint network, our technique minimizes a regularization func- tion that takes advantage of the biased image derivatives to yield reectance images less inuenced by shading.

Estimating Intrinsic Images from Image Sequences with Biased Illumination