Shape approximation: from multiview range images to parametric geons

We have studied the problem of deriving object part approximations by a new set of distinct volumetric shape types called parametric geons from multiview and single-view range data. This is accomplished by fitting the models to range data of single-part objects and then classifying the fitting residuals. We investigate how the number of object views can affect the ultimate shape approximation. Experimental results show that qualitative shape information can be recovered using data taken from single general views, and that multiview data significantly improve the accuracy of the quantitative model information