Improving model shape acquisition by incorporating geometric constraints

While the problem of model tting for 3-dimensional range data has been addressed with some success, the problem of increasing the accuracy of the whole t still remains. This paper describes a technique of global shape improvement based upon feature position and shape constraints. These constraints may be globally applied or inferred from general engineering principles. This paper describe a general , incremental, framework whereby constraints can be added and integrated in the model reconstruction process, resulting on optimal trade-oo between minimization of the shape tting error and the con-straint's tolerances.