Our goal is to reconstruct both the shape and reflectance properties of surfaces from multiple images. We argue that an object-centered representation is most appropriate for this purpose because it naturally accommodates multiple sources of data, multiple images (including motion sequences of a rigid object), self-occlusions, and geometric constraints. We then present a specific object-centered reconstruction method. It begins with an initial estimate of surface shape that is iteratively adjusted to minimize an objective function that combines information from multiple input images. The objective function is a weighted sum of `stereo,' shading, and smoothness components, where the weight varies over the surface. For example, the stereo component is weighted more strongly where the surface projects onto highly textured areas in the images, and less strongly otherwise. Thus, each component has its greatest influence where its accuracy is likely to be greatest. Experimental results on both synthetic and real images are presented.