Regularity selection for effective 3D object reconstruction from a single line drawing

Different regularities have been used in the reconstruction of a 3D object from a single-view line drawing. These regularities are not all equally informative in the reconstruction process: certain regularities may correspond mainly to noise, not information; some may overlap with each other or are not too relevant to the reconstruction. This paper studies these regularities comprehensively, so as to select the most effective set that can give robust and reliable 3D reconstruction. The selection is made through a method called automatic relevance determination (ARD), which employs the Bayesian framework and support vector regression estimation. The proposed method is able to identify the worst regularities according to their ARD parameters and eliminate them. The effectiveness of this pruning is evaluated by model validation. The regularity set so obtained is effective for general 3D reconstruction. The experimental results show that the regularity set selected can reduce the reconstruction complexity and produce satisfactory reconstruction performance.