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Depth from Focus (DFF) and Depth from Defocus (DFD) methods are theoretically uni ed with the geometric triangulation principle. Fundamentally, the depth sensitivities of DFF and DFD are not di erent than those of stereo (or motion) based systems having the same physical dimensions. Contrary to common belief, DFD does not inherently avoid the matching (correspondence) problem. Basically, DFD and DFF do not avoid the occlusion problem any more than triangulation techniques, but they are more stable in the presence of such disruptions. The fundamental advantage of DFF and DFD methods is the two-dimensionality of the aperture, allowing more robust estimation. We analyze the e ect of noise in di erent spatial frequencies, and derive the optimal changes of the focus settings in DFD. These results elucidate the limitations of methods based on depth of eld and provide a foundation for fair performance comparison between DFF/DFD and shape from stereo (or motion) algorithms.