Research Seiminar - September 05, 2003

From Surface Normals to Surfaces via Shapelets

Abstract:

Many shape measurement algorithms such as shape from shading, photometric stereo and shape from texture only return the surface normals of an object. The surface shape has to be inferred from these normals, typically via some integration process. However, reconstruction through the integration of surface normals is sensitive to noise, and to the choice of integration paths that are used across the surface. An additional difficulty is that the surface normals will often have an ambiguity of 180 degrees in the tilt component.

This talk presents a new approach to the reconstruction of surfaces from surface normals using basis functions, referred to here as shapelets . The surface gradients of the shapelets are correlated with the gradients of the surface and the correlations summed to form the reconstruction. This results in a simple reconstruction process that is very robust to noise. Where there is an ambiguity of 180 degrees in the surface tilt, reconstructions of reduced quality are still possible up to a positive/negative shape ambiguity. Intriguingly, some form of reconstruction is also possible using just slant, or just tilt information.