We evaluate the reliability of the 3-D (Euclidean) shape reconstructed from two uncalibrated perspective views. Introducing a statistical model of image noise, we optimally compute the fundamental matrix and evaluate its uncertainty in quantitative terms. We then evaluate the covariance matrices of the reconstructed 3-D points by propagating the image noise and the uncertainty in the fundamental matrix using a simple scheme. We show real-image experiments and discuss the effect of the “gauge” on the uncertainty description.