Dense Mirroring Surface Recovery from 1D Homographies and Sparse Correspondences

In this work, we recover the 3D shape of mirrors, sunglasses, and stainless steel implements. A computer monitor displays several images of parallel stripes, each image at a different angle. Reflections of these stripes in a mirroring surface are captured by the camera. For every image point, the direction of the displayed stripes and their reflections in the image are related by a 1D homography matrix, computed with a robust version of the statistically accurate heteroscedastic approach. By focusing on a sparse set of image points for which monitor-image correspondence is computed, the depth and the local shape may be estimated from these homographies. The depth estimation relies on statistically correct minimization and provides accurate, reliable results. Even for the image points where the depth estimation process is inherently unstable, we are able to characterize this instability and develop an algorithm to detect and correct it. After correcting the instability, dense surface recovery of mirroring objects is performed using constrained interpolation, which does not simply interpolate the surface depth values but also uses the locally computed 1D homographies to solve for the depth, the correspondence, and the local surface shape. The method was implemented and the shape of several objects was densely recovered at submillimeter accuracy.