  We present a novel method for reconstructing the shape of an object from
measured gradient data. A certain class of optical sensors does not measure the
shape of an object, but its local slope. These sensors display several
advantages, including high information efficiency, sensitivity, and robustness.
For many applications, however, it is necessary to acquire the shape, which
must be calculated from the slopes by numerical integration. Existing
integration techniques show drawbacks that render them unusable in many cases.
Our method is based on approximation employing radial basis functions. It can
be applied to irregularly sampled, noisy, and incomplete data, and it
reconstructs surfaces both locally and globally with high accuracy.
