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We introduce a new method to describe shape relationships over time in a photograph. We acquire both range and image information in a sequence of frames using a stationary stereo camera. From the pictures taken, we compute a composite image consisting of the pixels from the surfaces closest to the camera over all the time frames. Through occlusion cues, this composite reveals 3-D relationships between the shapes at different times. We call the composite a shape-time photograph. Small errors in stereo depth measurements can create artifacts in the shape-time images. We correct most of these using a Markov network to estimate the most probable front-surface pixel, taking into account (a) the stereo depth measurements and their uncertainties, and (b) spatial continuity assumptions for the time-frame assignments of the front-surface pixels.