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We address the problem of multiple view stereo from a perceptual organization perspective. Currently, the leading methods in the field are volumetric. They operate at the level of scene voxels and image pixels, without considering the structures depicted in them. On the other hand, many perceptual organization methods for binocular stereo are not extensible to more images. We present an approach where feature matching and structure reconstruction are addressed within the same framework. In order to handle noise, lack of image features, and discontinuities, we adopt a tensor representation for the data and tensor voting for information propagation. The key contributions are twofold. First, we introduce "saliency" instead of correlation as the criterion to determine the correctness of matches; second, our tensor representation and voting enable us to perform the complex computations associated with multiple view stereo at a reasonable computational cost. We present results on real data.