We present a method for the reconstruction of 3D planes from calibrated 2D images. Given a set of pixels Ω in a reference image, our method computes a plane which best approximates that part of the scene which has been projected to Ω by exploiting additional views. Based on classical image alignment techniques we derive linear matching equations minimally parameterized by the three parameters of an object-space plane. The resulting iterative algorithm is highly robust because it is able to integrate over large image regions due to the correct object-space approximation and hence is not limited to comparing small image patches. Our method can be applied to a pair of stereo images but is also able to take advantage of the additional information provided by an arbitrary number of input images. A thorough experimental validation shows that these properties enable robust convergence especially under the influence of image sensor noise and camera calibration errors.