This paper describes a novel approach for recovering the structure and motion of a rigid textured surface from an image sequence. Camera focal length is also recovered, yielding metric estimates of the structure without the need for pre-calibration. The key innovation is the use of local \em affine flow parameters as the measurements within an extended Kalman filter (EKF) estimation framework, in contrast to feature correspondences or optical flow used in previous approaches. This enables surface normals to be recovered in addition to depth, unlike a feature correspondence scheme, but without the computational limitation of an optical flow approach. The method is based on equating the affine parameters to a local linearisation of the 2-D motion field and using the EKF to provide recursive estimates of the 3-D structure and motion. Experiments on both synthetic and real sequences demonstrate that the approach has considerable potential.