Reconstruction Of Consistent Shape From Inconsistent Data: Optimization Of 21/2d Sketches

Although the 3D orientations of edges and surfaces are theoretically sufficient for reconstructing the 3D object shape, this does not mean that the 3D object shape can actually be reconstructed: Inconsistency may result if image data contain errors. We propose a scheme of oprimization to construct a consistent object shape from inconsistent data. Our optimization is achieved by solving a set of linear equations. This technique is first applied to the problem of shape from motion and then to the 3D recovery based on the rectangularity hypothesis and the parallelism hypothesis. 1. Constraints on 2%D Sketches In the past, various 3D shape recovery techniques called shape from ... have been proposed (shape from motion, shape from shading, shape from texture, etc.). Now, we must ask the following question: Do these techniques really enable us to recover the 3D object shape? The shape from ... paradigms usually present us with object images equipped with the following types of 3D information: (i) The surface gradient (p , q), or equivalently the unit surface nor