Estimating a 3D shape from 2D images is a classic computer vision problem. Shape from focus is a commonly used method for this purpose. With shape from focus, 3D depth is estimated using a so-called focus measure operator. Pixel focus follows a Gaussian-like distribution in which the location of the peak is an indicator of the 3D depth. Locating the peak in this distribution is complicated due to noise coming from various sources. We investigate the accuracy of some existing algorithms and introduce a new algorithm based on phase correlation. Phase correlation is a powerful method for finding correlations between signals, especially in a noisy environment. The accuracy and robustness to noise of the proposed method are tested and proven by applying it to synthetic data as well as measurements of a calibration target. The proposed method is over 30% more accurate than comparable methods, yet requires more computational effort. •Novel peak detection method for shape from focus.•Phase correlation can be used for precision shape from focus measurements.•Slower but more accurate compared to default methods.•3D Surface Reconstruction.