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Given a set of high-resolution images of a scene, it is often desirable to predict the scene's appearance from viewpoints not present in the original data for purposes of change detection. When significant 3-D relief is present, a model of the scene geometry is necessary for accurate prediction to determine surface visibility relationships. In the absence of an a priori high-resolution model (such as those provided by LIDAR), scene geometry can be estimated from the imagery itself. These estimates, however, cannot, in general, be exact due to uncertainties and ambiguities present in image data. For this reason, probabilistic scene models and reconstruction algorithms are ideal due to their inherent ability to predict scene appearance while taking into account such uncertainties and ambiguities. Unfortunately, existing data structures used for probabilistic reconstruction do not scale well to large and complex scenes, primarily due to their dependence on large 3-D voxel arrays. The work presented in this paper generalizes previous probabilistic 3-D models in such a way that multiple orders of magnitude savings in storage are possible, making high-resolution change detection of large-scale scenes from high-resolution aerial and satellite imagery possible. Specifically, the inherent dependence on a discrete array of uniformly sized voxels is removed through the derivation of a probabilistic model which represents uncertain geometry as a density field, allowing implementations to efficiently sample the volume in a nonuniform fashion.