Assume that two pixels are more likely to be the same color, the closer the voxels they represent are. This measure is intended to favor colors that are similar being grouped at the same depth. For example, if an object in front occludes an area of sky behind, the measure of smoothness favors the blue pixels all being grouped together at the same depth. The total measure of smoothness uses the distance between voxels as an estimate of the expected standard deviation of the color difference,   The total information content is then the sum,  The z component of each pixel must be chosen to give the minimum value for the information content. This will give the most likely depths at each pixel. The minimum total information measure is,  The depth functions for the left and right images are the pair,  Smoothness is a measure of how similar colors that are close together are. There is an assumption that objects are more likely to be colored with a small number of colors. So if we detect two pixels with the same color they most likely belong to the same object. The method described above for evaluating smoothness is based on information theory, and an assumption that the influence of the color of a voxel influences the color of nearby voxels according to the normal distribution on the distance between points. The model is based on approximate assumptions about the world. Another method based on prior assumptions of smoothness is auto-correlation. Smoothness is a property of the world. It is not inherently a property of an image. For example, an image constructed of random dots would have no smoothness, and inferences about neighboring points would be useless. Theoretically smoothness, along with other properties of the world should be learnt. This appears to be what the human vision system does. The minimization problem is NP-complete. This means a general solution to this problem will take a long time to reach. However methods exist for computers based on heuristics that approximate the result in a reasonable amount of time. Also methods exist based on neural networks. Efficient implementation of stereoscopic vision is an area of active research.