Learning scene and blur model for active chromatic depth from defocus

In this paper, we propose what we believe is a new monocular depth estimation algorithm based on local estimation of defocus blur, an approach referred to as depth from defocus (DFD). Using a limited set of calibration images, we directly learn image covariance, which encodes both scene and blur (i.e., depth) information. Depth is then estimated from a single image patch using a maximum likelihood criterion defined using the learned covariance. This method is applied here within a new active DFD method using a dense textured projection and a chromatic lens for image acquisition. The projector adds texture for low-textured objects, which is usually a limitation of DFD, and the chromatic aberration increases the estimated depth range with respect to a conventional DFD. Here, we provide quantitative evaluations of the depth estimation performance of our method on simulated and real data of fronto-parallel untextured scenes. The proposed method is then experimentally evaluated qualitatively using a 3D printed benchmark.