Gauge-based reliability analysis of 3D reconstruction from two uncalibrated perspective views

We evaluate the reliability of the 3D (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 3D 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