Learning Shape from Defocus

We present a novel method for inferring three-dimensional shape from a collection of defocused images. It is based on the observation that defocused images are the null-space of certain linear operators that depend on the threedimensional shape of the scene as well as on the optics of the camera. Unlike most current work based on inverting the imaging model to recover the "deblurred" image and the shape of the scene, we approach the problem from a new angle by collecting a number of deblurred images, and estimating the operator that spans their left null space directly. This is done using a singular value decomposition. Since the operator depends on the depth of the scene, we repeat the procedure for a number of different depths. Once this is done, depth can be recovered in real time: the new image is projected onto each null-space, and the depth that results in the smallest residual is chosen. The most salient feature of this algorithm is its robustness: not only can one lea...

• Type: Conference
• Year: 2002
• Where: ECCV
• Authors: Paolo Favaro, Stefano Soatto
• Keywords: Certain Linear Operators, Computer Vision, ECCV 2002, Radiance Distribution, Real Images, Singular Value Decomposition, Square-integrable Radiance Distributions